MegaMatcher SDK

Large-scale AFIS and Multi-Biometric Identification

MegaMatcher is designed for large-scale AFIS and multi-biometric systems developers. The technology ensures high reliability and speed of biometric identification even when using large databases.

Available as a software development kit that allows development of large-scale single- or multi-biometric fingerprint, iris, face, voice or palm print identification products for Microsoft Windows, Linux, macOS, iOS and Android platforms.

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Features and Capabilities
Proven in national-scale projects, including passport issuance and voter deduplication.
NIST MINEX-compliant fingerprint engine, NIST IREX iris engine.
Turnkey multi-biometric solution for national-scale identification projects with MegaMatcher ABIS.
High performance matching for large-scale systems with MegaMatcher Accelerator.
Fingerprints, irises and faces can be matched on smart cards using MegaMatcher On Card.
Includes fingerprint, iris, face, voice and palm print modalities.
Rolled, flat and latent fingerprint matching.
BioAPI 2.0 and other ANSI and ISO biometric standards support.
ICAO requirements compliancy check for face images.
Effective price/performance ratio, flexible licensing and free customer support.

Megamatcher Fingerprint Template Extraction and Matching Engine

Full MINEX Compliance. NIST has recognized MegaMatcher fingerprint algorithm as MINEX compliant and suitable for use in personal identity verification (PIV) program applications.
Rolled and flat fingerprints matching. The MegaMatcher fingerprint engine matches rolled and flat fingerprints between themselves. Typically, conventional "flat" fingerprint identification algorithms perform matching between flat and rolled fingerprints less reliably due to the specific deformations of rolled fingerprints. MegaMatcher allows flat-to-flat, flat-to-rolled or rolled-to-rolled fingerprint matching with a high degree of reliability and accuracy. The algorithm matches up to 200,000 flat fingerprint records per second on a single PC.
MegaMatcher includes fingerprint image quality determination, which may be used during enrollment to ensure that only the best quality fingerprint template will be stored in the database.
Spoof fingerprint detection. A deep learning based scanned fingerprint image classification is used to separate live/non-live fingerprints to detect finger presentation attack. This feature covers spoofing attempts performed with ecoflex, wood glue, latex and gelatin and is useful for fraud identification.
Template generalization is used to generate a better quality template from several fingerprints. Better quality templates result in a higher level of identification accuracy.
MegaMatcher is tolerant to fingerprint translation, rotation and deformation. It uses a proprietary fingerprint matching algorithm that identifies fingerprints even if they are rotated, translated or have deformations. Also, the matching algorithm has a special mode for the cases when some fingerprint records have incorrect resolution.
Adaptive image filtration algorithm eliminates noises, ridge ruptures and stuck ridges, and reliably extracting minutiae from even the poorest quality fingerprints in less than 1 second.

Megamatcher Face Template Extraction and Matching Engine

Template generalization is used to generate a better quality template from several face images. Better quality templates result in a higher level of identification accuracy
Tolerance to face position assures a level of enrollment convenience. MegaMatcher allows for 360 degrees of head roll. Head pitch can be up to 15 degrees in each direction from the frontal position. Head yaw can be up to 45 degrees in each direction from the frontal position.See technical specifications for more details.
Reliable face detection assures accurate enrollment from cameras, webcams and various scanned documents; faces may be enrolled from the scanned pages of passports or other types of documentation. When there are multiple faces present in a video or an image, they may be enrolled and processed simultaneously. Person's gender, facial feature points and basic emotions can be optionally detected. Also, partially occluded faces (i.e. persons wearing face masks or respirators) can be recognized without separate enrollment.
Facial attributes recognition. MegaMatcher can be configured to detect certain attributes during the face extraction – smile, open-mouth, closed-eyes, glasses, dark-glasses, beard and mustache.
Age estimation. MegaMatcher can optionally estimate person's age by analyzing the detected face in the image.
Live face detection. A conventional face identification system can be tricked by placing a photo in front of the camera. MegaMatcher is able to prevent this kind of security breach by determining whether a face in a video stream or single frame is "live" or a photograph. The liveness detection can be performed in passive mode, when the engine evaluates certain facial features, and in active mode, when the engine evaluates user's response to perform actions like blinking or head movements.
The biometric template record can contain several face samples belonging to the same person. These samples can be enrolled from different sources and at different times, thus allowing improvement in matching quality. For example a person might be enrolled with eyeglasses and without, or with different types of eyeglasses; with and without beard or mustache, etc.

Megamatcher Voice Template Extraction and Matching Engine

Text-dependent voice matching engine determines if a voice sample matches the template that was extracted from a specific phrase. During enrollment, one or more phrases are requested from the person being enrolled. Later that person may be asked to pronounce a specific phrase for verification. This method assures protection against the use of a covertly recorded random phrase from that person.
Two-factor authentication with a passphrase is performed when a person is asked to say a unique phrase (such as passphrase or an answer to a "secret question" that is known only by the person being enrolled). The overall system security increases as both voice authenticity and password are checked.
Text-independent voice matching engine uses different phrases for user enrollment and recognition. This method is more convenient, as it does not require each user to remember the passphrase. It may be combined with the text-dependent algorithm to perform faster text-independent search with further phrase verification using the more reliable text-dependent algorithm.
Automatic voice activity detection. The engine is able to detect when users start and finish speaking.
Liveness detection. A system may request each user to enroll a set of unique phrases. Later the user will be requested to say a specific phrase from the enrolled set. This way the system can ensure that a live person is being verified (as opposed to impostor who uses voice recording).
Several voice records with the same phrase may be stored to improve speaker recognition reliability. Certain natural voice variations (i.e. hoarse voice) or environment changes (i.e. office and outdoors) can be stored in the same template.

Megamatcher Iris Template Extraction and Matching Engine

NIST IREX proven reliability. MegaMatcher iris matching engine is based on VeriEye, recognized by NIST as one of the most reliably accurate iris recognition algorithms available.
Fast matching. The iris matching speed is up to 200,000 comparisons per second on a single PC. See technical specifications for more details.
Robust iris detection. Irises are detected even when there are obstructions to the image, visual noise and/or different levels of illumination. Lighting reflections, eyelids and eyelashes obstructions are eliminated. Images with narrowed eyelids or eyes that are gazing away are also accepted.
Automatic interlacing detection and correction results in maximum quality of iris feature templates from moving iris images.
Correct iris segmentation is obtained even when perfect circles fail, the centers of the iris inner and outer boundaries are different, iris boundaries are definitely not circles and even not ellipses or iris boundaries seem to be perfect circles.
Iris image quality determination and spoof prevention. The image quality estimation can be used during iris enrollment to ensure that only the best quality iris template will be stored into database. Also, cosmetic (decorative) contact lens, which obscure an iris with some artistic or fake iris texture and/or change iris color, can be detected.
Technology Awards

MINEX Evaluations by NIST

MINEX III evaluation was successfully passed in 2015. In 2019 Neurotechnology's fingerprint template generator algorithm has been ranked the first in the NIST MINEX interoperability category; the fingerprint matching algorithm has also been ranked as the front-runner in terms of interoperability and, when combined, the two have become the supreme accuracy, high speed fingerprint recognition system.
MINEX Ongoing evaluation was successfully passed in 2014. The second place in the Ongoing MINEX ranking for fingerprint matching algorithms was achieved. MegaMatcher technology was recognized by the NIST as fully MINEX compliant.

FVC-Ongoing Results

In 2020 MegaMatcher fingerprint recognition algorithm has shown the top result at the FVC-onGoing evaluation. The fingerprint extractor and matcher were ranked as the most accurate for both FV-STD-1.0 and FV-HARD-1.0 benchmarks.
In 2019 MegaMatcher palm print matching algorithm has shown the top result at the FVC-onGoing evaluation. The algorithm was the most accurate overall and fastest among the five most accurate matchers.

PFT II AND PFT III (PROPRIETARY FINGERPRINT TEMPLATE) EVALUATIONS

Different versions of Neurotechnology's fingerprint recognition algorithm were submitted to the NIST Proprietary Fingerprint Template Evaluation. The algorithm's template matching accuracy was among the best participants at the previous PFT II evaluation. Our latest submissions to the PFT II and the ongoing PFT III are in average the most accurate algorithms in all the experiments.

FpVTE (Fingerprint Vendor Technology Evaluations) by NIST

FpVTE 2012 – in 2015 NIST recognized Neurotechnology's fingerprint identification algorithm as one of the fastest and most accurate among the evaluation's participants.
FpVTE 2003 – one of the best reliability results in the Middle Scale Test were shown. Neurotechnology participated in FpVTE 2003 under the name Neurotechnologija.

IREX Evaluations by NIST

IREX 10 – in 2020 Neurotechnology's iris recognition algorithm has been judged by NIST as the second most accurate among the IREX 10 participants. The submitted algorithm featured much faster template creation and search time, and much smaller template size than the only more accurate contender.
IREX IX – in 2018 Neurotechnology's iris recognition algorithm has been judged by the NIST as the second most accurate among the participants. The accelerated version of the algorithm was nearly 50 times faster than any other matcher in the NIST IREX IX evaluation.
IREX IV – in 2013 Neurotechnology's iris recognition algorithm has been judged the by the NIST as one of the fastest and most accurate among the participants.
IREX III – in 2012 MegaMatcher iris matching algorithm was the second fastest and provided 3 times higher recognition accuracy than the only faster contender.

FRVT Ongoing

In 2018 Neurotechnology has been ranked among 8 most accurate face recognition algorithm vendors out of 39, with tenth most accurate algorithm out of 78 in the FRVT leaderboard. The submission was also ranked as one of the best in two difficult scenarios, with second most accurate result on a complex dataset collected from operational photos related to ongoing criminal investigations, and fourth most accurate result with unconstrained, photojournalism-style photos.

FIVE (Face in Video Evaluation)

In 2015 Neurotechnology face recognition engine to the NIST Face in Video Evaluation (FIVE). In average the submitted algorithm was ranked among top 8 most accurate face recognition algorithms out of 16 vendors.

WSQ 3.1 Certification by the FBI

In 2011 FBI certified Neurotechnology's implementation of WSQ image format support.
SDK Contents

MegaMatcher SDK is designed for development of large-scale AFIS or multi-biometric identification products. Fingerprint, face, iris, voice and palm print recognition engines are included in MegaMatcher 12.1 SDK.

MegaMatcher 12.1 SDK includes server-side software and a set of modules for developing client-side applications. .NET components are included for rapid development of client-side software. MegaMatcher 12.1 supports BioAPI 2.0. To ensure system compatibility with other software, WSQ component is available, as well as modules for conversion between MegaMatcher template and biometric standards.

MegaMatcher 12.1 is suitable not only for developing civil AFIS, but also for forensic AFIS applications, as it includes an API for latent fingerprint template editing. Latent fingerprint template editing is necessary in order to submit a latent fingerprint (for example, one taken from a crime scene) for the identification into AFIS. Also MegaMatcher is able to match rolled and flat fingerprints between themselves.

These types of MegaMatcher 12.1 SDK are available:
MegaMatcher 12.1 Standard SDK for developing a client/server based multi-biometric fingerprint-face-iris identification product. This SDK is suitable for network-based and web-based systems with database size ranging from several thousand records up to million records. The SDK includes ready-to-use server-side software and a set of components for developing client-side applications on Microsoft Windows, Android, iOS, Linux and macOS.
MegaMatcher 12.1 Extended SDK for developing a large-scale network-based AFIS or multi-biometric identification product. The SDK includes all components of MegaMatcher 12.1 Standard SDK and MegaMatcher Accelerator software, which can be used for fault-tolerant scalable cluster software for fast parallel matching, processing high number of identification requests and handling databases with practically unlimited size. This SDK also allows to develop network-based and web-based systems.

License Activation Options

The components are copy-protected. The following license activation options are available:

Serial numbers are used to activate licenses for particular MegaMatcher components on particular computer or device. The activation is done via the Internet or by email. After activation the network connection is not required for single computer license usage. Notes:
  • Activation by serial number is not suitable for ARM-Linux, except BeagleBone Black and Raspberry Pi 3 devices.
  • Activation by serial number is not suitable for virtual environments.
Internet activation . A special license file is stored on a computer or a mobile/embedded device; the license file allows to run particular MegaMatcher components on that computer or device after checking the license over the Internet. Internet connection x should be available periodically for a short amount of time. A single computer license can be transferred to another computer or device by moving the license file there and waiting until the previous activation expires.
Volume License Manager. Licenses may be stored in a volume license manager dongle . The license activation may be performed without connection to the Internet and is suitable for virtual environments. Volume license manager is used on site by integrators or end users to manage licenses for MegaMatcher components in the following ways:
  • Activating single computer licenses – An installation license for a MegaMatcher component will be activated for use on a particular computer. The number of available licenses in the license manager will be decreased by the number of activated licenses.
  • Managing single computer licenses via a LAN or the Internet – The license manager allows the management of installation licenses for MegaMatcher components across multiple computers or mobile/embedded devices in a LAN or over the Internet. The number of managed licenses is limited by the number of licenses in the license manager. No license activation is required and the license quantity is not decreased. Once issued, the license is assigned to a specific computer or device on the network.
  • Using license manager as a dongle – A volume license manager containing at least one license for a MegaMatcher component may be used as a dongle, allowing the MegaMatcher component to run on the particular computer where the dongle is attached.

Additional MegaMatcher component licenses for the license manager may be purchased at any time.

Licenses Validity

All SDK and component licenses are perpetual and do not have expiration. There are no annual fee or any other fees except license purchasing fee. It is possible to move licenses from one computer or device to another. Neurotechnology provides a way to renew the license if the computer undergoes changes due to technical maintenance.

SDK Components

The table below lists the biometric components which are included in MegaMatcher 12.1 Standard SDK and MegaMatcher 12.1 Extended SDK. The list can be narrowed with filtering by certain requirements based on the target biometric system.

Select the required biometric components:
MegaMatcher SDK components and licenses
Component types MegaMatcher 12.1 Standard SDK MegaMatcher 12.1 Extended SDK
Fingerprint component licenses included with a specific SDK:
Fingerprint Image Processing 1 single computer license 1 single computer license
Fast Fingerprint Matcher 1 single computer license 1 single computer license
MegaMatcher Accelerator Dev.Edit. (fingerprint engine) 1 single computer license
Fingerprint Extractor 1 single computer license 1 single computer license
Fingerprint Client 3 single computer license 3 single computer license
Fingerprint Matcher 1 single computer license 1 single computer license
Mobile Fingerprint Client 3 single computer license 3 single computer license
Mobile Fingerprint Extractor 1 single computer license 1 single computer license
Mobile Fingerprint Matcher 1 single computer license 1 single computer license
Mobile Fast Fingerprint Matcher Optionally available Optionally available
Face component licenses included with a specific SDK:
Face Image Processing 1 single computer license 1 single computer license
Fast Face Matcher 1 single computer license 1 single computer license
MegaMatcher Accelerator Dev.Edit. (face engine) 1 single computer license
Face Extractor 1 single computer license 1 single computer license
Face Client 3 single computer license 3 single computer license
Face Matcher 1 single computer license 1 single computer license
Mobile Face Client 3 single computer license 3 single computer license
Mobile Face Extractor 1 single computer license 1 single computer license
Mobile Face Matcher 1 single computer license 1 single computer license
Mobile Fast Face Matcher Optionally available Optionally available
Iris component licenses included with a specific SDK:
Iris Image Processing 1 single computer license 1 single computer license
Iris Face Matcher 1 single computer license 1 single computer license
MegaMatcher Accelerator Dev.Edit. (iris engine) 1 single computer license
Iris Extractor 1 single computer license 1 single computer license
Iris Client 3 single computer license 3 single computer license
Iris Matcher 1 single computer license 1 single computer license
Mobile Iris Client 3 single computer license 3 single computer license
Mobile Iris Extractor 1 single computer license 1 single computer license
Mobile Iris Matcher 1 single computer license 1 single computer license
Mobile Fast Iris Matcher license Optionally available Optionally available
Voice component licenses included with a specific SDK:
Voice Processing 1 single computer license 1 single computer license
Voice Extractor 1 single computer license 1 single computer license
Voice Matcher 1 single computer license 1 single computer license
Voice Client 3 single computer license 3 single computer license
Mobile Voice Client 3 single computer license 3 single computer license
Mobile Voice Extractor 1 single computer license 1 single computer license
Mobile Voice Matcher 1 single computer license 1 single computer license
Palm print component licenses included with a specific SDK:
Palmprint Image Processing 1 single computer license 1 single computer license
Fast Palm Print Matcher 1 single computer license 1 single computer license
MegaMatcher Accelerator Dev.Edit. (palmprint engine) 1 single computer license
Palm Print Client 1 single computer license 1 single computer license
Palm Print Matcher 1 single computer license 1 single computer license
Matching Server + +

MegaMatcher 12.1 SDK includes programming samples and tutorials that show how to use the components of the SDK to perform fingerprint, face and iris template extraction or matching against other templates. The samples and tutorials are available for these programming languages and platforms:

Windows 32 & 64 bit Linux 32 & 64 bit macOS Android iOS
Programming samples
C/C++ + + +
Objective-C +
C# +
Visual Basic .NET +
Java + + + +
Programming tutorials
C + + +
C++ + + +
C# +
Visual Basic .NET +
Java + + + +
Supported Scanners

We are always looking for scanner manufacturers to include the support for their fingerprint scanners to our products. Please, contact us for more details.

These fingerprint scanners and sensors are supported by our biometric products. Each device has 500 ppi resolution, unless a different resolution is mentioned in the Notes column. Please, click on a scanner name to view more information about it.

Scanner Sensor technology Capture method Image capture area Image size (pixels) Notes
3M Cogent CSD 330 Optical Touch 1" x 1" 500 x 500 FIPS 201 PIV certified.
Abilma UNITY Optical Roll or touch 5.0" x 5.1"   Scans palm prints, up to 4 flat fingerprints simultaneously or single rolled fingerprints;
FBI IQS compliant (Appendix F of EFTS);
Wi-Fi 802.11n connection.
ACS AET62 Capacitive Sweep 0.38" x 0.01" 192 x 4 Embedded contactless smart card reader
ACSAET65 Capacitive Sweep 0.38" x 0.01" 192 x 4 Embedded smart card reader
Aratek A400 Capacitive Touch 0.5" x 0.7" 256 x 360 PIV and Appendix F Mobile ID FAP 10 certified.
Aratek A600 Optical Touch 0.6" x 0.8 300 x 400 PIV and Appendix F Mobile ID FAP 20 certified.
Aratek BM5510 Optical Touch 0.6" x 0.9" 320 x 480 Wi-Fi 802.11 b/g/n connection.
3G connection.
Bluetooth 4.0 connection.
Aratek BM7500 Optical Touch 0.6" x 0.9" 320 x 480 Wi-Fi 802.11 b/g/n connection.
3G connection.
Bluetooth 4.0 connection.
Aratek RO900 Optical Roll or touch 3.2" x 3.0" 1600 x 1500 Appendix F compliant;
Scans up to 4 flat fingerprints simultaneously.
ARH AFS 510 Optical Roll or touch 4" x 3"   IAFIS IQS compliant.
Scans up to 4 flat finger simultaneously.
Athena ASEDrive IIIe Combo Bio F2 Capacitive Touch 0.4" x 0.6" 208 x 288 Embedded smart card reader
BioID BioSlap Optical Roll or touch 3.2" x 3.0"   FBI Appendix F certified. IP54 rated casing.
BioLink U-Match MatchBook v.3.5 Optical Touch 0.6" x 0.9"    
Biometrika Fx2000 Optical Touch 0.5" x 1.0" 296 x 560  
Biometrika Fx2100 Optical Touch 0.5" x 1.0"   PIV certified.
Biometrika Fx3000 Optical Touch 0.7" x 1.0" 400 x 560  
Biometrika HiScan Optical Touch 1.0" x 1.0" 500 x 500 PIV / FIPS 201 certified
Biometrika HiScan PRO Optical Touch 1.0" x 1.0"   PIV certified.
Credence ID Credence One Capacitive Touch 0.5" x 0.7" 256 x 360 Wi-Fi 802.11 a/b/g/n connection.
Bluetooth 4.0 connection.
Embedded smart card reader.
Credence ID CredenceTWO-R LES Touch 0.8" x 1.0" 400 x 500 Wi-Fi 802.11 a/b/g/n connection.
Bluetooth 4.0 connection.
3G/4G/LTE connection.
Embedded smart card reader.
Credence ID Trident LES Roll or touch 1.6" x 1.5"   Scans single or dual flat fingerprints, or single rolled fingerprints.
Wi-Fi 802.11 a/b/g/n connection.
Bluetooth 4.0 connection.
Integrated dual iris scanner.
Cross Match Guardian 100 Optical Touch 3.2" x 3.3"   FBI Appendix F certified;
Scans up to 4 flat fingerprints simultaneously.
Cross Match Guardian 200 Optical Touch 3.2" x 3.3"   FBI Appendix F certified;
Scans up to 4 flat fingerprints simultaneously and Rolled fingerprints.
Cross Match Guardian 300 Optical Touch 3.2" x 3.3"   FBI Appendix F certified;
Scans up to 4 flat fingerprints simultaneously and Rolled fingerprints.
Cross Match Guardian FW Optical Roll or touch 3.2" x 3.0"   FBI Appendix F certified;
Scans flat ten-prints and single rolled fingerprints.
FireWire connection.
Cross Match Guardian Module Optical Touch 3.2" x 3.3"   FBI Appendix F certified;
Scans up to 4 flat fingerprints simultaneously and Rolled fingerprints.
Cross Match Guardian USB Optical Roll or touch 3.2" x 3.0"   FBI Appendix F certified;
Scans flat ten-prints and single rolled fingerprints.
Cross Match Guardian-F USB Optical Touch 3.2" x 3.0"   FBI Appendix F certified;
Scans up to 4 flat fingerprints simultaneously.
Cross Match L Scan 500P Optical Roll or touch 5.0" x 5.1" 2496 x 2560 Scans palm prints, up to 4 flat fingerprints simultaneously or single rolled fingerprints;
FBI IQS compliant (Appendix F of EFTS)
Cross Match Patrol Optical Roll or touch 3.2" x 3.0"   FBI Appendix F certified;
Scans rolled fingerprints;
Scans up to 4 flat fingerprints simultaneously.
Cross Match Patrol ID Optical Touch 3.2" x 3.0"   FBI Appendix F certified;
Scans up to 4 flat fingerprints simultaneously.
Cross Match Verifier 300 Classic Optical Touch 1.2" x 1.2"    
Cross Match Verifier 300 LC Optical Touch 1.2" x 1.2"    
Cross Match Verifier 300 LC 2.0 Optical Touch 1.2" x 1.2"    
Cross Match Verifier 320 LC Optical Roll or touch 1.6" x 1.5"   FBI IAFIS IQS and PIV certified;
Scans 2 flat fingerprints simultaneously or 1 rolled.
DERMALOG F1 Optical Touch 0.7"x1.0"   FIPS 201 PIV certified.
DERMALOG LF10 Optical Roll or touch 3.2" x 3.2"   FBI IQS Appendix F certified.
Captures 4+4+2 flat fingerprints, and single rolled.
DERMALOG ZF1 Optical Touch 0.6" x 0.9" 320 x 480 PIV / FIPS 201 certified;
Includes live finger detection
DigitalPersona EikonTouch 710 Capacitive Touch 0.5" x 0.7" 256 x 360 FIPS 201 certified
DigitalPersona U.are.U 4000 Module Optical Touch 0.6" x 0.7"    
DigitalPersona U.are.U 4000 scanner Optical Touch 0.6" x 0.7"    
DigitalPersona U.are.U 4500 scanner Optical Touch 0.6" x 0.7"    
DigitalPersona U.are.U 5100 Module Optical Touch 0.5" x 0.6"   FIPS 201 PIV certified.
DigitalPersona U.are.U 5100 Reader Optical Touch 0.5" x 0.6"   FIPS 201 PIV certified.
DigitalPersona U.are.U 5160 Reader Optical Touch 0.5" x 0.6"   FIPS 201 PIV certified.
DigitalPersona U.are.U 5200 Module Optical Touch 0.6" x 0.8"   FIPS 201 PIV certified.
DigitalPersona U.are.U 5300 Module Optical Touch 0.8" x 1.0"   PIV and Appendix F Mobile ID FAP 30 certified.
DigitalPersona U.are.U 5300 Reader Optical Touch 0.8" x 1.0"   PIV and Appendix F Mobile ID FAP 30 certified.
DigitalPersona (UPEK) Eikon Solo Capacitive Sweep 1.0" x 0.3"    
DigitalPersona (UPEK) EikonTouch 300 Capacitive Touch 0.4" x 0.6" 208 x 288  
DigitalPersona (UPEK) EikonTouch 500 Capacitive Touch 0.5" x 0.7" 256 x 360  
DigitalPersona (UPEK) EikonTouch 700 Capacitive Touch 0.5" x 0.7" 256 x 360 FIPS 201 certified
Famoco FX100 Bio Optical Touch 0.6" x 0.9"   Mobile stand-alone device. Wi-Fi, 3G and Bluetooth connections. Embedded contactless RFID card reader.
Fujitsu MBF200 Capacitive Touch 0.5" x 0.6" 256 x 300  
Fulcrum Biometrics mobileOne QuickDock Optical Touch 0.5" x 0.7" 256 x 360 Wi-Fi 802.11 b/g/n connection.
Apple Lightning connector.
PIV / FIPS 201 compliant sensor.
Futronic eFAM (FS84) Optical Touch 0.6" x 0.9" 320 x 480 Ethernet or serial connection.
Futronic FS10 Optical Touch 1.0" x 1.0" 500 x 500 PIV / FIPS 201 certified
Futronic FS26 Optical Touch 0.6" x 0.9" 320 x 480 Embedded MIFARE card reader/writer
Futronic FS26EU Optical Touch 0.6" x 0.9" 320 x 480 PIV / FIPS 201 compliant. Embedded smart card reader (ISO7816 contact and ISO14443/Mifare contactless supported)
Futronic FS28 Optical Touch 0.6" x 0.9" 320 x 480 Bluetooth connection
Futronic FS50 Optical Roll or touch 1.6" x 1.5" 800 x 750 Two finger scanner. FBI IQS certified, FIPS 201 / PIV compliant.
Futronic FS60> Optical Roll or touch 3.2" x 3.0" 1600 x 1500 Four finger scanner. FBI IQS IAFIS certified.
Futronic FS64 Optical Roll or touch 3.2" x 3.0" 1600 x 1500 Scans up to 4 flat fingerprints simultaneously or single rolled fingerprints;
FBI IQS compliant (Appendix F of EFTS).
Futronic FS80 Optical Touch 0.6" x 0.9" 320 x 480 Includes spoof detection
Futronic FS80H Optical Touch 0.6" x 0.9" 320 x 480 Includes spoof detection
Futronic FS82 Optical Touch 0.6" x 0.9" 320 x 480 Includes spoof detection
Embedded smart card reader
Futronic FS88 Optical Touch 0.6" x 0.9" 320 x 480 PIV / FIPS 201 certified
Futronic FS88H Optical Touch 0.6" x 0.9" 320 x 480 PIV / FIPS 201 certified
Futronic FS90 Optical Touch 0.6" x 0.9" 300 x 440  
Green Bit DactyID20 Optical Touch 0.7" x 0.8"   PIV-certified.
Green Bit DactyScan40i Optical Roll or touch 1.6" x 1.6"   FBI Appendix F and FIPS-201 / PIV certified.
Scans dual flat fingerprints.
Green Bit DactyScan84c Optical Roll or touch 3.2" x 3.0   FBI IQS compliant;
Scans up to 4 flat fingerprints simultaneously.
Green Bit DactyScan84n Optical Roll or touch 3.2" x 3.0   FBI IQS compliant;
Scans up to 4 flat fingerprints simultaneously.
Green Bit MultiScan527 Optical Roll or touch 5.0" x 5.0"   Scans palm prints, up to 4 flat fingerprints simultaneously or single rolled fingerprints;
FBI IQS compliant (Appendix F of EFTS).
HFSecurity HF-4000 Optical Touch 0.8" x 1.3"    
HFSecurity HF-7000 Capacitive Touch 0.5" x 0.6" 256 x 288  
Hongda S500 Optical Touch 0.6" x 0.8"    
Hongda S680 Optical Roll or touch 1.6" x 1.6"    
Hongda S700 Optical Touch 3.2" x 3.0 1600 x 1500 Scans 4 flat fingerprints simultaneously.
IAFIS IQS compliant
IDENTOS Tactivo Mini for Android Optical Optical Touch 0.5" x 0.8" 256 x 360 Embedded smart card reader. IP54 rated casing.
iMD SF202 Capacitive Touch 0.4" x 0.6" 208 x 288  
iMD SF302GM Capacitive Touch 0.5" x 0.7" 256 x 288  
Integrated Biometrics Columbo LES Touch 0.8" x 1.0" 400 x 500 PIV and Appendix F Mobile ID FAP 30 certified.
Integrated Biometrics Columbo OEM LES Touch 0.8" x 1.0" 400 x 500 PIV and Appendix F Mobile ID FAP 30 certified.
Integrated Biometrics Curve LES Touch 0.6" x 0.7" 288 x 352 Includes live finger detection
Integrated Biometrics Five-0 LES Touch 3.2" x 2.0" 1600 x 1000 Scans up to 4 flat fingerprints simultaneously. Appendix F Mobile ID FAP 50 certified.
Integrated Biometrics Kojak LES Roll or touch 3.2" x 3.0" 1600 x 1500 Scans up to 4 flat fingerprints simultaneously or single rolled fingerprints;
FBI IQS compliant (Appendix F of EFTS).
Integrated Biometrics LES650 LES Touch 0.6" x 0.7"   Includes live finger detection
Integrated Biometrics Sherlock LES Roll or touch 1.6" x 1.5" 800 x 750 Two finger scanner. PIV and Appendix F Mobile ID FAP 45 certified.
Integrated Biometrics Watson LES Roll or touch 1.6" x 1.5" 800 x 750 Two finger scanner. PIV and Appendix F Mobile ID FAP 45 certified.
Integrated Biometrics Watson Mini LES Roll or touch 1.6" x 1.5" 800 x 750 Two finger scanner. PIV and Appendix F Mobile ID FAP 45 certified.
Jenetric LIVETOUCH QUATTRO Optical Roll or touch 3.2" x 3.0"   FBI Appendix F certified;
Scans up to 4 flat fingerprints simultaneously and Rolled fingerprints.
Koehlke KIAU-5110B3 Optical Touch 0.5" x 0.6" 240 x 288  
L-1 DFR 2080 Optical Touch 0.6" x 0.6" 248 x 292  
L-1 DFR 2090 Optical Touch 0.8" x 1.0" 425 x 484 USB and RS-170 (analog) image output
L-1 DFR 2100 Optical Touch 1" x 1" 500 x 500 PIV / FIPS 201 certified
L-1 DFR 2300 Optical Touch 1.5" x 1.2    
Lumidigm M300 sensor Optical Touch 0.6" x 0.7"   Includes live finger detection.
Lumidigm M301 sensor Optical Touch 0.6" x 0.7"   Includes live finger detection.
Lumidigm M311 sensor Optical Touch 0.6" x 0.7"   Includes live finger detection.
Lumidigm M321 sensor Optical Touch 0.6" x 0.7"   Includes live finger detection.
Lumidigm V371 reader Optical Touch 0.7" x 1.1"   Includes live finger detection. Embedded NFC reader.
Lumidigm Venus V300 OEM module Optical Touch 0.7" x 1.1"   Includes live finger detection.
Lumidig Venus V302 reader Optical Touch 0.7" x 1.1"   Includes live finger detection.
Miaxis SM-2BU Capacitive Touch      
NeuBio MARS 02 Optical Touch 0.6" x 0.7" 252 x 330  
NEXT Biometrics NB-3010-U Thermal Touch 0.5" x 0.7" 180 x 256 385 ppi resolution
NEXT Biometrics NB-3023-U2 Thermal Touch 0.5" x 0.7" 180 x 256 385 ppi resolution
NEXT Biometrics NB-65200-U Thermal Touch 0.6" x 0.8" 300 x 400 PIV certified
NITGEN eNBioScan-C1 Optical Touch 0.6" x 0.7" 260 x 330  
NITGEN eNBioScan-D plus Optical Roll or touch 1.9" x 1.9" 952 x 952 Two finger scanner. Appendix F Mobile ID FAP 45 certified.
NITGEN eNBioScan-F Optical Touch 1.2" x 1.2" 600 x 600 PIV / FIPS 201 certified;
FBI IQS compliant.
NITGEN Fingkey Hamster Optical Touch 0.7" x 0.8"    
NITGENFingkey Hamster II Optical Touch 0.7" x 0.8"   Includes live finger detection
NITGEN Fingkey Mouse III Optical Touch 0.7" x 0.8"   Mouse with embedded fingerprint reader
NITGEN NScan-T Optical Roll or touch 3.2" x 3.0"   FBI Appendix F certified;
Scans up to 4 flat fingerprints simultaneously and Rolled fingerprints.
SecuGen Hamster III Optical Touch 0.6" x 0.7" 260 x 300  
SecuGen Hamster IV Optical Touch 0.6" x 0.7" 258 x 336 PIV / FIPS 201 certified;
FBI IAFIS IQS compliant.
SecuGen Hamster Plus Optical Touch 0.6" x 0.7" 260 x 300  
SecuGen Hamster Pro Optical Touch 0.7" x 0.9" 260 x 300  
SecuGen Hamster Pro 20 Optical Touch 0.7" x 0.9" 300 x 400 PIV and Appendix F Mobile ID FAP 20 certified.
SecuGen Hamster Pro Duo CL Optical Touch 0.7" x 0.9" 300 x 400 PIV and Appendix F Mobile ID FAP 20 certified. Embedded NFC reader
SecuGen Hamster Pro Duo SC/PIV Optical Touch 0.7" x 0.9" 300 x 400 PIV and Appendix F Mobile ID FAP 20 certified. Embedded smart card reader
SecuGen iD-USB SC Optical Touch 0.6" x 0.7" 260 x 300 Embedded smart card reader
SecuGen iD-USB SC/PIV Optical Touch 0.6" x 0.7" 258 x 336 Embedded smart card reader
Shanghai Fingertech BIOCA-111 Optical Touch 0.7" x 0.9"    
SMUFS Biometric SMUFS BT Capacitive Touch 0.5" x 0.7" 256 x 360 Bluetooth connection.
PIV / FIPS 201 compliant fingerprint sensor.
Startek FC320U Optical Roll or touch 0.9" x 0.9" 450 x 450 PIV / FIPS 201 compliant.
Startek FM220U Optical Touch 0.5" x 0.6" 264 x 324  
Startek FPC360U Capacitive Touch 0.5" x 0.7" 256 x 360 Dongle form-factor.
Suprema BioMini Optical Touch 0.6" x 0.7" 288 x 320  
Suprema BioMini Combo Optical Touch 0.7" x 1.0" 320 x 480 Embedded smart card reader. PIV / FIPS 201 compliant fingerprint sensor.
Suprema BioMini Plus Optical Touch 0.6" x 0.7" 260 x 340 FIPS-201 (PIV) compliant
Suprema BioMini Plus2 Optical Touch 0.6" x 0.7" 315 x 354 FIPS-201 (PIV) compliant
Suprema BioMini SFU-S20 Optical Touch 0.7" x 1.0" 320 x 480 PIV and Appendix F Mobile ID FAP 20 certified.
Suprema BioMini Slim Optical Touch 0.7" x 1.0" 320 x 480 PIV and Appendix F Mobile ID FAP 20 certified.
Suprema BioMini Slim 2 Optical Touch 0.6" x 0.8" 300 x 400 PIV and Appendix F Mobile ID FAP 20 certified.
Suprema BioMini Slim 3 Optical Touch 0.8" x 1.0" 400 x 500 PIV and Appendix F Mobile ID FAP 30 certified.
Suprema RealScan G1 Optical Touch 1.0" x 1.0" 500 x 500 PIV-certified
Suprema RealScan-10 Optical Roll or touch 3.2" x 3.0" 1600 x 1500 The scanner is able to scan up to 4 flat fingerprints simultaneously, or a single rolled fingerprint.
Suprema RealScan-D Optical Roll or touch 1.8" x 1.8" 900 x 900 FBI IAFIS IQS certified;
Scans 2 flat fingerprints simultaneously or 1 rolled fingerprint
Suprema RealScan-F Optical Roll or touch 5.1" x 5.1" 2550 x 2550 Scans palm prints, up to 4 flat fingerprints simultaneously or single rolled fingerprints;
FBI IQS compliant (Appendix F of EFTS).
Suprema RealScan-FC Optical Roll or touch 5.2" x 5.1" 2500 x 2500 Scans palm prints.
Suprema RealScan-G10 Optical Roll or touch 3.2" x 3.0"   The scanner is able to scan up to 4 flat fingerprints simultaneously, or a single rolled fingerprint.
Suprema RealScan-G10F Optical Touch 3.2" x 3.0"   The scanner is able to scan up to 4 flat fingerprints simultaneously.
Suprema SFR300-S Optical Touch 0.6" x 0.7" 288 x 288  
Suprema SFU300 Optical Touch 0.6" x 0.7" 288 x 320  
TENBIO TOUCH ONE Optical Touch 0.8" x 1.0" 340 x 380  
Testech Bio-i CYTE Hybrid Touch 0.6" x 0.7"    
Thales Cogent CSD101i Optical Touch 0.5" x 0.7" 256 x 360 PIV and Appendix F Mobile ID FAP 10 certified.
TopLink Pacific BLUEFiN Capacitive Touch 0.5" x 0.7" 256 x 360 Bluetooth connection
TST Biometrics BiRD 3 Optical Non-contact 0.6" x 0.7" 480 x 640 Includes live finger detection;
Optional ethernet connection.
UnionCommunity ViRDI FOH02SC Optical Touch 0.6" x 0.7"   Embedded contactless smart card reader
UPEK Eikon Capacitive Sweep 1.0" x 0.4"    
UPEK Eikon To Go Capacitive Sweep 1.0" x 0.4"    
UPEK TouchChip TCRU1C Capacitive Touch 0.5" x 0.7" 256 x 360  
UPEK TouchChip TCRU2C Capacitive Touch 0.4" x 0.6" 208 x 288  
ZKSoftware ZK4000 Optical Touch 0.6" x 0.7" 280 x 360  
ZKSoftware ZK4500 Optical Touch 0.6" x 0.8" 280 x 360  
ZKSoftware ZK6000 Optical Touch 0.6" x 0.7"    
ZKSoftware ZK7000 Optical Touch 0.6" x 0.7"    
ZKSoftware ZK8000 Optical Touch 0.6" x 0.7"   Embedded MIFARE card reader / writer
ZKTeco SLK20R Optical Touch 0.6" x 0.8 300 x 400  
ZKTeco ZK9500 Optical Touch 0.6" x 0.8 300 x 400  
Zvetco Verifi P5100 Capacitive Touch 0.5" x 0.7" 256 x 360 Based on PIV / FIPS 201 certified UPEK TCS1 sensor

Supported Face Capture Cameras

These cameras are supported by MegaMatcher SDK:
  • Any webcam or camera that is accessible using:
    • DirectShow, Windows Media or Media Foundation interfaces for Microsoft Windows platform.
    • GStreamer interface for Linux or Mac platform.
  • Any built-in smartphone or tablet camera that is supported by iOS or Android OS. The camera should have at least 0.3 MegaPixel (640 x 480 pixels) resolution.
  • Any IP camera, that supports RTSP (Real Time Streaming Protocol):
    • Only RTP over UDP is supported.
    • VLC framework can be optionally used for reading video streams.
    • H.264/MPEG-4 AVC or Motion JPEG should be used for encoding the video stream.
  • These advanced cameras are supported:
    • Akiyama Akys-10 Biometric Camera
    • CMITech EMX-30 – face & iris camera (Microsoft Windows only)
    • Iris ID iCAM R100 and iCAM TD100 – face & iris cameras (Microsoft Windows only)
    • VistaFA2 / VistaFA2E / VistaEY2 face & iris cameras (Microsoft Windows only)
  • These models of still cameras are supported:
    • Canon EOS family still cameras (Microsoft Windows only; the supported camera models are EOS M50, EOS 2000D, EOS 4000D, EOS M100, EOS 6D Mark II, EOS 200D, EOS 77D, EOS 800D, EOS M6, EOS M5, EOS 5D Mark IV, EOS-1D X Mark II, EOS 80D, EOS 1300D, EOS M10, EOS 5DS, EOS 5DS R, EOS 760D, EOS 750D, EOS 7D Mark II)
    • Nikon DSLR still cameras (Microsoft Windows only; a specific camera model should support video capture and should be listed there)
    • Fujifilm X-T2 still camera (Microsoft Windows only)
  • Cameras, which can operate in near-infrared spectrum, can be used for image capture. MegaMatcher/VeriLook algorithm is able to match faces, captured in near-infrared spectrum, against faces, captured in visible light. See the VeriLook algorithm testing results for details.
  • Integrators can also write plug-ins to support their cameras using the plug-in framework provided with the Device Manager from the MegaMatcher SDK.

Simultaneous capture from multiple cameras is possible.

A video file can be also used as a data source for applications based on MegaMatcher SDK.

Supported Iris Scanners

The table below explains which eye iris scanners are supported by MegaMatcher SDK under different operating systems.

We are always looking for scanners' manufacturers to include the support for their iris scanners to our products. Please, contact us for more details.

Integrators or scanner manufacturers can also write plug-ins for the Device Manager from the MegaMatcher SDK to support their iris cameras using the provided plug-in framework. The SDK documentation contains more information about the plug-in framework.

  Microsoft Windows
7 / 8 / 10		 Linux Android
32 bit 64 bit 32 bit 64 bit
CMITech BMT-20 / EMX-30 + +      
Credence ID Trident         +(1)
HID Crossmatch I Scan 2 / Crossmatch I Scan 3 + +(2)      
Iris ID iCAM R100 / iCAM T10 / iCAM TD100 + +      
Iritech IriShield USB MK 2120U / IriShield-USB BK 2121U + + + + +
Iritech IriMagic1000BK + +(2)      
Mantra MIS100V2         +
VistaFA2 / VistaFA2E / VistaEY2 / VistaEY2-02 / VistaEY2R iris & face cameras + +      
VistaEY2H iris camera + +      

Notes:
(1) The device has pre-installed Android OS.
(2) Can be used on 64-bit OS, but only in 32-bit applications.

System Requirements

There are specific requirements for running specific components on particular platforms

System Requirements for MegaMatcher Client-Side Components for PC or Mac

PC or laptop with x86-64 (64-bit) compatible processors.
0.6 seconds are required to create a template with a single fingerprint, face, iris or voiceprint record using Intel Core i7-8700K processor running at 3.5 GHz. See the technical specifications for more details.
4 seconds are required to create a template from a full palm print image on Intel Core i7-8700K processor running at 3.5 GHz.
AVX2 support is highly recommended. Processors that do not support AVX2 will still run the MegaMatcher algorithms, but in a mode, which will not provide the specified performance. Most modern processors support this instruction set, but please check if a particular processor model supports it.
Page Content:
Client-side components for PC or Mac
Client-side components for Android
Client-side components for iOS
Client-side components for ARM Linux
Server-side fast template extraction components
Matching Server
x86 (32-bit) processors can still be used, but the algorithm will not provide the specified performance.
2 GB of free RAM is recommended for general usage scenarios. It is possible to reduce RAM usage for particular scenarios.
Optionally, depending on biometric modalities and requirements:
A fingerprint scanner. MegaMatcher SDK includes support modules for more than 160 models of fingerprint scanners under Microsoft Windows, Linux and macOS platforms.
A webcam or IP camera or any other camera (recommended frame size: 640 x 480 pixels) for face images capturing. MegaMatcher SDK includes support modules for a list of cameras. An IP camera should support RTSP and stream video in H.264 or M-JPEG. Cameras, which can operate in near-infrared spectrum, can be also used for image capture. Any other webcam or camera should provide DirectShow, Windows Media or Media Foundation interfaces for Windows platform, GStreamer interface for Linux and Mac platforms.
An iris camera (recommended image size: 640 x 480 pixels) for iris image capture. MegaMatcher SDK includes support modules for several iris cameras.
A microphone. Any microphone that is supported by the operating system can be used.
A palm print scanner.
A flatbed scanner for fingerprint or palm print data capturing from paper can be used. 500 ppi or 1000 ppi FBI certified scanners are recommended. MegaMatcher SDK includes a programming sample, which shows how to use a flatbed scanner on Microsoft Windows platform.
Integrators can also write plug-ins to support their biometric capture devices using the plug-in framework provided with the Device Manager from the MegaMatcher SDK.
Network/LAN connection (TCP/IP) for communication with Matching Server or MegaMatcher Accelerator unit(s). MegaMatcher client-side components can be used without network if they are used only for data collection.
Linux specific requirements:
Linux 3.10 or newer kernel is required. If a fingerprint scanner is required, note that some scanners have only 32-bit support modules and will work only from 32-bit applications.
glibc 2.17 or newer
GStreamer 1.10.x or newer with gst-plugin-base and gst-plugin-good is required for face capture using camera/webcam or rtsp video.
libgudev-1.0 219 or newer (for camera and/or microphone usage)
alsa-lib 1.1.6 or newer (for voice capture
gcc 4.8 or newer (for application development)
GNU Make 3.81 or newer (for application development)
Java SE JDK 8 or newer (for application development with Java)
Microsoft Windows specific requirements:
Microsoft Windows 7 / 8 / 10.
Microsoft .NET framework 4.5 (for .NET components usage)
Microsoft Visual Studio 2012 or newer (for application development with C++ / C# / VB .NET)
Java SE JDK 8 or newer (for application development with Java)
macOS specific requirements:
macOS (version 10.12.6 or newer)
XCode 6.x or newer (for application development)
GStreamer 1.10.x or newer with gst-plugin-base and gst-plugin-good is required for face capture using camera/webcam or rtsp video.
GNU Make 3.81 or newer (to build samples and tutorials development)
Java SE JDK 8 or newer (for application development with Java)

System Requirements for MegaMatcher Client-Side Components for Android

A smartphone or tablet that is running Android 5.0 (API level 21) OS or newer.
If you have a custom Android-based device or development board, contact us to find out if it is supported.
ARM-based 1.5 GHz processor recommended for processing a fingerprint, face, iris or voiceprint in the specified time. Slower processors may be also used, but the processing of fingerprints, faces, irises and voiceprints will take longer time.
At least 1 GB of free RAM should be available for the application. Additional RAM is required for applications that perform 1-to-many identification, as all biometric templates need to be stored in RAM for matching. See the technical specifications for the templates sizes with specific biometric modalities.
Optionally, depending on biometric modalities and requirements:
A fingerprint reader. MegaMatcher is able to work with several supported fingerprint readers under Android OS. Integrators may also use image files or receive image data from external devices like flatbed scanners or other stand-alone cameras.
A camera for face capture. MegaMatcher is able to work with all cameras that are supported by Android OS. At least 0.3 MegaPixel (640 x 480 pixels) camera is required for the MegaMatcher biometric algorithm. Integrators may also use image files or receive image data from external devices like flatbed scanners or stand-alone cameras.
A microphone. MegaMatcher is able to work with all microphones that are supported by Android OS. Integrators may also use audio files or receive audio data from external devices.
An iris scanner. A project may require to capture iris images using some hand-held devices:
MegaMatcher SDK includes support modules for several iris cameras under Android OS.
MegaMatcher technology also accepts irises for further processing as BMP, JPG PNG or WebP images, thus almost any third-party iris capturing hardware can be used with the MegaMatcher technology if it generates image in the mentioned formats.
Integrators may implement the iris scanner support by themselves or use the software provided by the scanners manufacturers. The integrators should note, that the most accurate iris recognition is achievable only when iris images are captured with near-infrared cameras and appropriate illumination. However, it is still possible to recognize irises with reasonable accuracy, when the irises are captured with cameras, which are built in smartphones or tablets, using proper illumination and focus, and choosing proper environment.
Network connection. A MegaMatcher-based mobile application may require network connection for activating the MegaMatcher component licenses. See the list of available activation options in the licensing model for more information. Also, network connection may be required for client/server applications.
PC-side development environment requirements:.
Java SE JDK 8 (or higher)
AndroidStudio 4.0 IDE
AndroidSDK 21+ API level
Gradle 6.1.1 build automation system or newer
Android Gradle Plugin 4.0.0
Internet connection for activating MegaMatcher component licenses

System Requirements for MegaMatcher Client-Side Components for iOS

One of the following devices, running iOS 11.0 or newer:
  • iPhone 5S or newer iPhone.
  • iPad Air or newer iPad models.
At least 1 GB of free RAM should be available for the application. Additional RAM is required for applications that perform 1-to-many identification, as all biometric templates need to be stored in RAM for matching. See the technical specifications for the templates sizes with specific biometric modalities.
Optionally, depending on biometric modalities and requirements:
A fingerprint scanner. MegaMatcher is able to work with several supported fingerprint readers under Android OS. Integrators may also use image files or receive image data from external devices like flatbed scanners or other stand-alone cameras.
A camera for face capture. MegaMatcher is able to work with all cameras that are supported by Android OS. At least 0.3 MegaPixel (640 x 480 pixels) camera is required for the MegaMatcher biometric algorithm. Integrators may also use image files or receive image data from external devices like flatbed scanners or stand-alone cameras.
A microphone. MegaMatcher is able to work with all microphones that are supported by Android OS. Integrators may also use audio files or receive audio data from external devices.
An iris scanner. At the moment iris scanner support on iOS platform should be implemented by integrators. The integrators should note, that the most accurate iris recognition is achievable only when iris images are captured with near-infrared cameras and appropriate illumination. However, it is still possible to recognize irises with reasonable accuracy, when the irises are captured with cameras, which are built in smartphones or tablets, using proper illumination and focus, and choosing proper environment.
MegaMatcher technology also accepts fingerprint, face and iris images for further processing as BMP, JPG PNG or WebP files, thus almost any third-party biometric capturing hardware can be used with the MegaMatcher technology if it generates images in the mentioned formats.
Network connection. A MegaMatcher-based mobile application may require network connection for activating the MegaMatcher component licenses. See the list of available activation options in the licensing model for more information. Also, network connection may be required for client/server applications.
Development environment requirements:
  • a Mac running macOS 10.12.6 or newer.
  • Xcode 9.x or newer.

System Requirements for MegaMatcher Client-Side Components for ARM Linux

We recommend to contact us and report the specifications of a target device to find out if it will be suitable for running MegaMatcher-based applications.

There is a list of common requirements for ARM Linux platform:
  • A device with ARM-based processor, running Linux 3.2 kernel or newer.
  • ARM-based 1.5 GHz processor recommended for fingerprint processing in the specified time.
    • ARMHF architecture (EABI 32-bit hard-float ARMv7) is required.
    • Lower clock-rate processors may be also used, but the fingerprint, face, iris or voiceprint processing will take longer time.
  • At least 1 GB of free RAM should be available for the application. Additional RAM is required for applications that perform 1-to-many identification, as all biometric templates need to be stored in RAM for matching. See the technical specifications for the templates sizes with specific biometric modalities.
  • Optionally, depending on biometric modalities and requirements:
    • A fingerprint scanner. MegaMatcher is able to work with several supported fingerprint readers under ARM Linux OS. Integrators may also use image files or receive image data from external devices like flatbed scanners or other stand-alone cameras.
    • A camera for face capture. At least 0.3 MegaPixel (640 x 480 pixels) camera is required for the MegaMatcher biometric algorithm. These cameras are supported by MegaMatcher on ARM Linux platform:
      • Any camera which is accessible using GStreamer interface.
      • Any IP camera, that supports RTSP (Real Time Streaming Protocol):
        • Only RTP over UDP is supported.
        • H.264/MPEG-4 AVC or Motion JPEG should be used for encoding the video stream.
    • An iris scanner. At the moment iris scanner support on ARM Linux platform should be implemented by integrators. The integrators should note, that the most accurate iris recognition is achievable only when iris images are captured with near-infrared cameras and appropriate illumination. However, it is still possible to recognize irises with reasonable accuracy, when the irises are captured with regular cameras, using proper illumination and focus, and choosing proper environment.
    • A microphone. Any microphone that is supported by the operating system can be used.
    • Fingerprint, face or iris images in BMP, JPG PNG or WebP formats can be processed by the MegaMatcher technology.
  • glibc 2.17 or newer.
  • GStreamer 1.10.x or newer with gst-plugin-base and gst-plugin-good is required for face capture using camera/webcam or rtsp video.
  • alsa-lib 1.1.6 or newer (for voice capture)
  • libgudev-1.0 219 or newer (for camera and/or microphone usage)
  • Network/LAN connection (TCP/IP) for client/server applications. Also, network connection is required for using the Matching Server component.
  • Development environment specific requirements:
    • gcc 4.8 or newer
    • GNU Make 3.81 or newer
    • Java SE JDK 8 or newer

System Requirements for Server-Side Fast Template Extraction Components

  • Server hardware with at least these processors (see the technical specifications for more details):
    • Dual Intel Xeon Gold 6126 (2.6 GHz) processors for extracting a template from single fingerprint, face or palmprint images in the specified time;
    • Single Intel Xeon Gold 6126 (2.6 GHz) processor for extracting templates from single iris image, or voice samples in the specified time.
    The processors should support AVX2.
  • at least 8 GB of free RAM should be available for the high-volume server application.
  • Network/LAN connection (TCP/IP) for communication with client-side applications, Matching Server or MegaMatcher Accelerator unit(s).
  • Linux specific requirements:
    • Linux 3.10 or newer kernel is required.
    • glibc 2.17 or newer
    • GStreamer 1.10.x or newer with gst-plugin-base and gst-plugin-good (for face capture using rtsp video)
  • Microsoft Windows specific requirements:
    • Microsoft Windows Server 2003 / Server 2008 / Server 2008 R2 / Server 2012, 64-bit.
    • Microsoft .NET framework 4.5 (for .NET components usage)

System Requirements for Matching Server

  • PC, Mac or server with x86 (32-bit) or x86-64 (64-bit) compatible CPU.
    • 64-bit platform must be used when large databases (more than 2.5 million fingerprints or more than 1 million users with 2 fingerprints and 1 face enrolled) used and 3 GB RAM is not enough for templates storing in RAM.
    • Intel Core i7-8700K (3.5 GHz) processor or better is recommended.
    • AVX2 support is highly recommended. Processors that do not support AVX2 will still run the MegaMatcher algorithms, but in a mode, which will not provide the specified performance. Most modern processors support this instruction set, but please check if a particular processor model supports it.
  • Enough free RAM for Matching Server code, matching engines and templates. See the technical specifications for the templates sizes with specific biometric modalities.
  • Database engine or connection with it. Usually a DB engine required for the Matching Server is running on the same computer. MegaMatcher SDK contains support modules for:
    • Microsoft SQL Server (only for Microsoft Windows platform);
    • PostgreSQL (Microsoft Windows and Linux);
    • MySQL (Microsoft Windows and Linux);
    • Oracle (Microsoft Windows and Linux);
    • SQLite (all platforms);
    • memory DB (all platforms).
    The fastest option is memory DB but it does not support relational queries, therefore the recommended option is SQLite, as it requires less resources than other options but provides enough functionality.
  • Network/LAN connection (TCP/IP) for the communication with client-side applications.
  • Linux specific requirements:
    • Linux 3.10 or newer kernel is required.
    • glibc 2.17 or newer
  • Microsoft Windows specific requirements:
    • Microsoft Windows 7 / 8 / 10 / Server 2008 / Server 2008 R2 / Server 2012.
  • macOS specific requirements:
    • macOS (OS X 10.9 or newer macOS version)
Technical Specifications

All biometric templates should be loaded into RAM before identification, thus the maximum biometric templates database size is limited by the amount of available RAM.

Fingerprint Engine Specifications

Fingerprint scanners are recommended to have at least 500 ppi resolution and at least 1" x 1" fingerprint sensors. The specifications are provided for 500 x 500 pixels fingerprint images and templates extracted from these images.

MegaMatcher fingerprint template extraction and matching algorithm is designed to run on multi-core processors allowing to reach maximum possible performance on the used hardware.

MegaMatcher 12.1 fingerprint engine specifications
  Embedded / mobile (1)
platform 		PC-based (2)
platform 		Server
platform
Template extraction components Mobile
Fingerprint
Extractor 		Mobile
Fingerprint
Client 		Fingerprint
Extractor 		Fingerprint
Client 		Fingerprint
Image
Processing(3)
Template extraction speed
(fingerprints per minute) 		45 50 45 100 3,000
Template matching components Mobile
Fingerprint
Matcher 		Mobile
Fast
Fingerprint
Matcher 		Fingerprint
Matcher 		Fast
Fingerprint
Matcher(2)
Template matching speed
(fingerprints per second) 		3,000 200,000 40,000 200,000
Single fingerprint record size in a template (4) (bytes) 800 - 8,000
(configurable)

Face Engine Specifications and Recommendations

General recommendations for facial recognition:
  • Face recognition accuracy of the MegaMatcher algorithm heavily depends on the quality of a face image. Image quality during enrollment is important, as it influences the quality of the face template.
  • 32 pixels is the recommended minimal distance between eyes for a face on image or video stream to perform face template extraction reliably. 64 pixels or more recommended for better face recognition results. Note that this distance should be native, not achieved by resizing an image.
  • Several images during enrollment are recommended for better facial template quality which results in improvement of recognition quality and reliability.
  • Additional enrollments may be needed when facial hair style changes, especially when beard or mustache is grown or shaved off.
  • Persons wearing face masks or respirators can be recognized without separate enrollment. Face quality check should be disabled for this scenario.
The face recognition engine has certain tolerance to face posture:
  • head roll (tilt) – ±180 degrees (configurable);
    • ±15 degrees default value is the fastest setting which is usually sufficient for most near-frontal face images.
  • head pitch (nod) – ±15 degrees from frontal position.
    • The head pitch tolerance can be increased up to ±25 degrees if several views of the same face that covered different pitch angles were used during enrollment.
  • head yaw (bobble) – ±90 degrees from frontal position (default value).
    • Smaller yaw tolerance values are not recommended to be used except if the target system does not meet the system requirements.
    • Several views of the same face can be enrolled to the database to cover the whole ±90 degrees yaw range from frontal position.
Face liveness check:
  • A stream of consecutive images (usually a video stream from a camera) is required for the live face detection.
  • When the liveness check is enabled, it is performed by the face engine before feature extraction. If the face in the stream fails to qualify as "live", the features are not extracted.
  • Only one face should be visible in these frames.
  • Users can enable these liveness check modes:
    • Active – the engine requests the user to perform certain actions like blinking or moving one's head. All requested actions should be performed to pass the liveness check. This mode can work with both colored and grayscale images. 5 frames per second or better frame rate required.
    • Passive – the engine analyzes certain facial features while the user stays still in front of the camera for a short period of time. Colored images are required for this mode. 10 frames per second or better frame rate is required. Better score is achieved when users do not move at all.
    • Passive then active – the engine first tries the passive liveness check, and if it fails, tries the active check. This mode requires colored images.
    • Simple – the engine requires user to turn head from side to side while looking at camera. This mode can work with both colored and grayscale images. 5 frames per second or better frame rate recommended.
    • Single frame passive – the engine uses a neural network to estimate if a face image is not inserted in front of a camera using a paper photo or smartphone screen. This mode does not need any interaction from the user.

The specifications below are provided for the default roll and yaw values.

MegaMatcher face template extraction and matching algorithm is designed to run on multi-core processors allowing to reach maximum possible performance on the used hardware.

MegaMatcher 12.1 face engine specifications
  Embedded / mobile (1)
platform 		PC-based (2)
platform 		Server
platform
Template extraction components Mobile
Face
Extractor 		Mobile
Face
Client 		Face
Extractor 		Face
Client 		Face
Image
Processing(3)
Template extraction speed
(faces per minute) 		45 50 45 100 3,000
Template matching components Mobile Face Matcher Mobile
Fast
Face
Matcher 		Face Matcher Fast Face Matcher (2)
Template matching speed
(faces per second) 		3,000 200,000 40,000 200,000
Single face record size in a template (4) (bytes) 194 or 322
(configurable)

Iris Engine Specifications

Iris capture cameras are recommended to produce at least 640 x 480 pixels images. The specifications are provided for these images.

MegaMatcher iris template extraction and matching algorithm is designed to run on multi-core processors allowing to reach maximum possible performance on the used hardware.

MegaMatcher 12.1 iris engine specifications
  Embedded / mobile (1)
platform 		PC-based (2)
platform 		Server
platform
Template extraction components Mobile
Iris
Extractor		 Mobile
Iris
Client		 Iris
Extractor		 Iris
Client		 Iris
Image
Processing(5)
Template extraction speed
(irises per minute)		 45 50 45 100 3,000
Template matching components Mobile Iris Matcher Mobile
Fast
Iris
Matcher		 Iris Matcher Fast Iris Matcher(2)
Template matching speed
(irises per second) 		3,000 200,000 40,000 200,000
Single iris record size in a template (4) (bytes) 2,486

Voiceprint Engine Specifications and Recommendations

  • General recommendations:
    • The speaker recognition accuracy of MegaMatcher depends on the audio quality during enrollment and identification.
    • Voice samples of at least 2-seconds in length are recommended to assure speaker recognition quality.
    • A passphrase should be kept secret and not spoken in an environment where others may hear it if the speaker recognition system is used in a scenario with unique phrases for each user.
    • The text-independent speaker recognition may be vulnerable to attack with a covertly recorded phrase from a person. Passphrase verification or two-factor authentication (i.e. requirement to type a password) will increase the overall system security.
  • Microphones – there are no particular constraints on models or manufacturers when using regular PC microphones, headsets or the built-in microphones in laptops, smartphones and tablets. However these factors should be noted:
    • The same microphone model is recommended (if possible) for use during both enrollment and recognition, as different models may produce different sound quality. Some models may also introduce specific noise or distortion into the audio, or may include certain hardware sound processing, which will not be present when using a different model. This is also the recommended procedure when using smartphones or tablets, as different device models may alter the recording of the voice in different ways.
    • The same microphone position and distance is recommended during enrollment and recognition. Headsets provide optimal distance between user and microphone; this distance is recommended when non-headset microphones are used.
    • Web cam built-in microphones should be used with care, as they are usually positioned at a rather long distance from the user and may provide lower sound quality. The sound quality may be affected if users subsequently change their position relative to the web cam.
  • Sound settings:
    • Settings for clear sound must be ensured; some audio software, hardware or drivers may have sound modification enabled by default. For example, the Microsoft Windows OS usually has, by default, sound boost enabled.
    • A minimum 11025 Hz sampling rate, with at least 16-bit depth, should be used during voice recording.
  • Environment constraints – the MegaMatcher speaker recognition engine is sensitive to noise or loud voices in the background; they may interfere with the user's voice and affect the recognition results. These solutions may be considered to reduce or eliminate these problems:
    • A quiet environment for enrollment and recognition.
    • Several samples of the same phrase recorded in different environments can be stored in a biometric template. Later the user will be matched against these samples with much higher recognition quality.
    • Close-range microphones (like those in headsets or smartphones) that are not affected by distant sources of sound.
    • Third-party or custom solutions for background noise reduction, such as using two separate microphones for recording user voice and background sound, and later subtracting the background noise from the recording.
  • User behavior and voice changes:
    • Natural voice changes may affect speaker recognition accuracy:
      • a temporarily hoarse voice caused by a cold or other sickness;
      • different emotional states that affect voice (i.e. a cheerful voice versus a tired voice);
      • different pronunciation speeds during enrollment and identification.
    • The aforementioned voice and user behavior changes can be managed in two ways:
      • separate enrollments for the altered voice, storing the records in the same person's template;
      • a controlled, neutral voice during enrollment and identification.

MegaMatcher voiceprint template extraction and matching algorithm is designed to run on multi-core processors allowing to reach maximum possible performance on the used hardware.

MegaMatcher 12.1 voiceprint engine specifications
  Embedded / mobile (1)
platform 		PC-based
platform 		Server
platform
Template extraction components Mobile
Voice
Extractor		 Mobile
Voice
Client		 Voice
Extractor(2) 		Voice
Client(2) 		Voice
Processing(5)
Template extraction speed
(voiceprints per minute)		 45 50 45 100 3,000
Template matching components Mobile Voice Matcher Voice Matcher(2)
Template matching speed
(voiceprints per second)		 100 8,000
Single voiceprint record size in a template (4) (6) (bytes) 3,500 - 4,500

Palmprint Engine Specifications

MegaMatcher palm print template extraction and matching algorithm is designed to run on multi-core processors allowing to reach maximum possible performance on the used hardware.

MegaMatcher 12.1 palm print engine specifications
  PC-based
platform 		Server
platform
Template extraction component Palm Print Client(2) Palm Print Image Processing(3)
Template extraction speed
(palm prints per minute)		 15 350
Template matching component Palm Print Matcher Fast Palm Print Matcher(2)
Template matching speed
(palm prints per second)		 800 4,000
Average single palm print record size in a template (4) (kilobytes) 33

Notes:
(1) Requires to be run on iOS devices or Android devices based on at least Snapdragon S4 system-on-chip with Krait 300 processor (4 cores, 1.51 GHz).
(2) Requires to be run on PC or laptop with at least Intel Core i7-8700K quad-core processor (3.5 GHz) to reach the specified performance.
(3) Requires to be run on server hardware with at least Dual Intel Xeon Gold 6126 processors (2.6 GHz) to reach the specified performance.
(4) MegaMatcher 12.1 allows to store multiple biometric records of the same or different biometric modalities in a template; in this case the template size is the sum of all included biometric records.
(5) Requires to be run on server hardware with at least Intel Xeon Gold 6126 processor (2.6 GHz) to reach the specified performance.
(6) The specifications are provided for 5-second long voice samples; template size has linear dependence from voice sample length.

Reliability Tests

The identification reliability is important for large-scale systems. MegaMatcher SDK includes a fused algorithm for fast and reliable identification using several biometric records taken from the same person.

As we do not have any single database with all supported biometric modalities, separate tests with selected modalities were performed for the MegaMatcher biometric engines to demonstrate their reliability and performance with single biometric modalities and combinations of several modalities:

Fingerprint, Face and Iris atching Engines Tests

The tests with MegaMatcher biometric fingerprint, face and iris matching engines and fused template matching algorithm were performed using Neurotechnology internal multi-biometric database:
  • The database had 7,500 sets of biometric records; each set contained 1 face, 2 irises and 10 fingerprints representing a unique person.
  • 1,500 unique persons were represented in the database.
  • 5 capture sessions were performed for each person.
The tests were performed with these biometric template types:
  • 1 fingerprint record extracted from left index fingerprint image.
  • 1 face record.
  • 1 iris record extracted from left eye image.
  • 2 fingerprint records extracted from same person's left and right index fingerprint images.
  • 2 iris records extracted from same person's different eye images.
  • 1 fingerprint + 1 face records – left index fingerprint and face taken from the same person.
  • 1 face + 1 iris records – left iris and face taken from the same person.
  • 1 fingerprint + 1 iris records – left index fingerprint and left iris taken from the same person.
  • 1 fingerprint + 1 face + 1 iris records – left index fingerprint, left iris and face taken from the same person.
The biometric engines had these parameters set:
  • ±90 degrees fingerprint rotation tolerance value was used for template matching;
  • ±15 degrees iris rotation tolerance value was used for template matching.
Two tests were performed with each template type:
  • Test 1 maximized matching accuracy. MegaMatcher 12.1 fused algorithm reliability in this test is shown as blue curves on the ROC charts.
  • Test 2 maximized matching speed. MegaMatcher 12.1 fused algorithm reliability in this test is shown as red curves on the ROC charts.

The tests with templates. which contained 1 fingerprint + 1 face + 1 iris records, resulted with 0 % FRR for all FAR values

Receiver operation characteristic (ROC) curves are usually used to demonstrate the recognition quality of an algorithm. ROC curves show the dependence of false rejection rate (FRR) on the false acceptance rate (FAR).

1 fingerprint
ROC chart: MegaMatcher 12.1 fingerprint matching algorithm
Click to zoom 		1 face
ROC chart: MegaMatcher 12.1 face matching algorithm
Click to zoom 		1 iris
ROC chart: MegaMatcher 12.1 iris matching algorithm
Click to zoom
     

Note that the tests with these biometric template types resulted with 0 % FRR for all FAR values, thus their charts are not shown:

  • 2 fingerprint records
  • 2 iris records
  • 1 fingerprint + 1 face records
  • 1 fingerprint + 1 iris records
  • 1 face + 1 iris records
  • 1 fingerprint + 1 face + 1 iris records
MegaMatcher 12.1 template matching engines reliability testing results
A template contains these biometric records FRR at 0.001 % FAR FRR at 0.0001 % FAR
Test 1 Test 2 Test 1 Test 2
1 fingerprint 0.0067 % 0.0633 % 0.0233 % 0.0867 %
1 face 0.3067 % 0.3400 % 0.3333 % 0.3733 %
1 iris 0.0600 % 0.0867 % 0.0733 % 0.1233 %
2 fingerprints 0.0000 % 0.0000 % 0.0000 % 0.0000 %
2 irises 0.0000 % 0.0000 % 0.0000 % 0.0000 %
1 fingerprint + 1 face 0.0000 % 0.0000 % 0.0000 % 0.0000 %
1 fingerprint + 1 iris 0.0000 % 0.0000 % 0.0000 % 0.0000 %
1 face + 1 iris 0.0000 % 0.0000 % 0.0000 % 0.0000 %
1 fingerprint + 1 face + 1 iris 0.0000 % 0.0000 % 0.0000 % 0.0000 %

These tests show that a large-scale automated biometric identification system based on MegaMatcher provides high identification reliability when using fingerprints, using fused same-biometric (different fingerprints or irises from the same person) matching significantly reduces FRR, and using multi-biometric identification results in a significant reliability increase.

Voiceprint and Face Matching Engines Tests

The tests with MegaMatcher biometric face and voiceprint matching engines, and the fused template matching algorithm were performed using face images and voice samples from the XM2VTS Database:
  • 295 unique persons were represented in the database.
  • 8 capture sessions were performed for each person.
  • The phrase 1 from the database was used for the testing, meaning that the same fixed phrase was used for all subjects.
The tests were performed with these biometric template types:
  • 1 face record.
  • 1 voiceprint record.
  • 1 voiceprint + 1 face records taken from the same person.
1 voiceprint + 1 face
MegaMatcher ROC chart calculated using face images and voice samples from XM2VTS database
Click to zoom

Receiver operation characteristic (ROC) curves are usually used to demonstrate the recognition quality of an algorithm. ROC curves show the dependence of false rejection rate (FRR) on the false acceptance rate (FAR).

MegaMatcher 12.1 face, voiceprint and fused template matching engines tests
  1 face
in a template		 1 voiceprint
in a template		 1 voiceprint
+ 1 face
in a template
FRR at 0.001 % FAR 0.0000 % 11.2300 % 0.0121 %
FRR at 0.0001 % FAR 0.0121 % 20.4300 % 0.0242 %

Palm Print Engine Tests

The MegaMatcher palm print template matching algorithm reliability tests were performed using internal palm print images database. The database contained 1,993 images of right hand full palms and 1,996 images of left hand full palms. The database represented 1,000 unique persons.
MegaMatcher Palm Print ROC chart calculated using internal palm print images database
Click to zoom

Receiver operation characteristic (ROC) curves are usually used to demonstrate the recognition quality of an algorithm. ROC curves show the dependence of false rejection rate (FRR) on the false acceptance rate (FAR). The chart with ROC curves for the MegaMatcher palm print template matching algorithm are available on the right.

High Productivity System Architecture

MegaMatcher SDK is intended for large-scale AFIS / ABIS projects and includes specialized components and biometric engines for biometric data capture, template extraction and matching. Some of the components are designed to provide high performance during large number of requests and/or large databases with millions of biometric templates, whereas others provide easy deployment on client sites for a reasonable price. Also, certain components are intended for building systems with lower performance requirements.

MegaMatcher SDK provides easy system scalability and allows to start a biometric system from one or two computers/servers system at the beginning, with further scaling up together with project capacity and speed requirements by using components with higher capabilities or adding more installations of the component connected to the same system..

These system architectures and components are usually used for specific projects:
Template creation on client-side and matching on server-side – recommended for AFIS, border control, various ID issuing systems, such as passports, ID cards, voter registration.
Template creation and matching on server side – recommended for online banking, government e-services and other mass scale systems, in which requests can be submitted by any device or computer.
Deduplication after all users data collected – recommended for ID issuing systems, which have previously collected biometric data, such as voter or population registry cleaning.
Template creation and matching on the same computer or device – recommended for stand-alone deployments like desktop or mobile, civil or forensic identification system.

A combination of the mentioned architectures and components can be also used within a large-scale biometric system to reach optimal performance and/or availability.

MegaMatcher Automated Biometric Identification System, an integrated multi-biometric solution for national-scale identification projects, can be also considered. The solution can be customized by Neurotechnology for specific project needs.

Template Creation On Client-Side and Matching On Server-Side

This is the most often used architecture for AFIS / ABIS, border control, various ID issuing systems, such as passports, ID cards or voter registration. It is suitable for various systems, ranging from small LAN-based systems to national-scale projects. The chart below shows the key components need for this architecture.

MegaMatcher template extraction components are used by integrators to develop client-side desktop or mobile applications. The components include all necessary functionality and performance for biometric data capture and template extraction for sending them to the server-side. The applications deployment needs only additional licenses for the corresponding components for each computer or device running the application.

MegaMatcher matching components can be easily scaled up at any time for higher performance based on the project requirements. The matching components are provided as ready-to-use Matching Server or MegaMatcher Accelerator 12.1 units with biometric engines for matching fingerprint, palmprint, face and iris templates.

Template Creation and Matching On Server Side

This architecture is designed to be used for biometric systems, which need to process requests from a very large number of clients in scenarios like online banking or government e-services , as well as other mass scale systems with very large number of users. The chart below shows the key components needed for this architecture.

MegaMatcher biometric data capture components provide necessary functionality for client-side applications, which acquire biometric images from scanners or cameras and send them to the server-side for further template extraction. Applications deployment needs only additional licenses for the corresponding components for each computer or device running the application. Integrators can also implement image capture by themselves and send images to the server-side part of the system. In this case client-side applications deployment does not need any licenses for Neurotechnology components.

MegaMatcher template extraction components are deployed on the server-side of the biometric system. The integrators need to develop application logic, which will operate with the template extraction components.

MegaMatcher matching components can be easily scaled up at any time for higher performance based on the project requirements. The components can be optionally deployed and are provided as ready-to-use Matching Server or MegaMatcher Accelerator 12.1 units with biometric engines for matching fingerprint, palmprint, face and iris templates.

Deduplication After All Users Data Collected

MegaMatcher template extraction components may need to be deployed on the server-side, as usually the biometric data is captured as fingerprint, palmprint, face or iris images, which need to be processed into biometric templates. The integrators need to develop application logic, which will operate with the template extraction components.

MegaMatcher template extraction components may need to be deployed on the server-side, as usually the biometric data is captured as fingerprint, palmprint, face or iris images, which need to be processed into biometric templates. The integrators need to develop application logic, which will operate with the template extraction components.

MegaMatcher matching components can be easily scaled up at any time for higher performance based on the project requirements. The components are provided as ready-to-use Matching Server or MegaMatcher Accelerator 12.1 units with biometric engines for matching fingerprint, palmprint, face and iris templates. Integrators will need to develop simple application logic for sending the biometric templates for for many-to-many deduplication and generating the duplicates search report. Note, that database deduplication task requires a lot of computational resources, as it needs to compare every biometric template with every other biometric template in a database.

Product Advisor can provide an estimation of possible components and their quantities based on the actual duplicates search project requirements

You may also consider the MegaMatcher ABIS Cloud Service, which provides results for a reasonable price without the need to develop a solution.

Template Creation and Matching On the Same Computer or Device

This architecture is designed for stand-alone biometric systems, which need to perform all tasks locally on the same computer or mobile device. The chart below shows the key components need for this architecture.

MegaMatcher template extraction and matching components are used by integrators to develop stand-alone biometric applications for desktop or mobile platforms. The components provide all necessary functionality and performance for biometric data capture, template extraction, multi-biometric identification or verification, as well as support for biometric standards and formats. Smaller systems can be also developed with single-biometrics SDKs.

The applications deployment requires only licenses for the used biometric components.

Megamatcher Server-Side Biometric Image Processing Components

Biometric image processing components for server-side
Image processing speed
Fingerprints 3,000
fingerprints per minute
Faces 3,000
fingerprints per minute
Irises 3,000
fingerprints per minute
Palmprints 350
fingerprints per minute

Megamatcher Scalable Server-Side Matching Components

MegaMatcher matching components are provided as ready-to-use Matching Server or MegaMatcher Accelerator 12.1 units with biometric engines for matching fingerprint, face and iris templates:

The Matching Server is intended to be used in moderatesize systems like local AFIS or multi-biometric system which do not have strict requirements on performance or availability. Matching Server software is provided with MegaMatcher 12.1 Standard SDK.
MegaMatcher Accelerator 12.1 is a solution for large-scale AFIS and multi-biometric projects, which is available as Development Edition, Standard, Extended and Extreme versions . The MegaMatcher Accelerator includes cluster software to enable system scalability, high availability and fault tolerance.MegaMatcher Accelerator software is provided with MegaMatcher 12.1 Extended SDK..
Template matching components performance and scalabilty
Database capacity Matching speed
Matching Server with Matcher engines
Fingerprints Unlimited 40,000

fingerprints per second
Faces Unlimited 40,000

faces per second
Irises Unlimited 40,000

irises per second
Palmprints Unlimited 800

palmprints per second

Matching Server with Fast Matcher engines
Fingerprints Unlimited 200,000

fingerprints per second
Faces Unlimited 200,000

faces per second
Irises Unlimited 200,000

irises per second
Palmprints Unlimited 4,000

palmprints per second

Cluster of MegaMatcher Accelerator 12.1 Development Edition with N units
Fingerprints N × 4,000,000

fingerprints

 N × 1,000,000

fingerprints per second
Faces N × 1,000,000

faces

 N × 1,000,000

faces per second
Irises N × 5,000,000

irises

 N × 1,000,000

irises per second
Palmprints N × 400,000

palmprints

 N × 20,000

palmprints per second

Cluster of MegaMatcher Accelerator 12.1 Standard with N units
Fingerprints N × 4,000,000

fingerprints

 N × 35,000,000

fingerprints per second
Faces N × 1,000,000

faces

 N × 35,000,000

faces per second
Irises N × 5,000,000

irises

 N × 70,000,000

irises per second
Palmprints N × 400,000

palmprints

 N × 600,000

palmprints per second

Cluster of MegaMatcher Accelerator 12.1 Extended with N units
Fingerprints N × 40,000,000

fingerprints

 N × 100,000,000

fingerprints per second
Faces N × 10,000,000

faces

 N × 100,000,000

faces per second
Irises N × 50,000,000

irises

 N × 200,000,000

irises per second
Palmprints N × 4,000,000

palmprints

 N × 2,000,000

palmprints per second

Cluster of MegaMatcher Accelerator 12.1 Extreme with N units
Fingerprints N × 160,000,000

fingerprints

 N × 1,200,000,000

fingerprints per second
Faces N × 40,000,000

faces

 N × 1,200,000,000

faces per second
Irises N × 200,000,000

irises

 N × 1,200,000,000

irises per second
Palmprints

Palmprint engine is not available
in MegaMatcher Accelerator Extreme Edition

Recommendations:

MegaMatcher Accelerator Development Edition has no limitations on cluster size, but in general it makes no sense to run more than 3 nodes in the cluster, as the whole system will cost like one MegaMatcher Accelerator Standard unit while providing lower performance.
MegaMatcher Accelerator Standard has no limitations on cluster size, but in general it makes no sense to run more than 2 nodes in the cluster, as the whole system will cost like one MegaMatcher Accelerator Extended unit while providing lower performance and capacity.
MegaMatcher Accelerator Extended has no limitations on cluster size, but in general it makes no sense to run more than 4 nodes in the cluster, as the whole system will cost like one MegaMatcher Accelerator Extreme unit while providing lower performance and capacity.
The matching speeds are provided for single-biometrics engines. If a template in a database contains multi-biometric entries, like fingerprint and face records belonging to the same person, the matching components will match proportionally lower number of persons' biometric database entries per second. See the Product Advisor for the estimated matching components based on the contents of biometric template and performance requirements.