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, Mac OS X, iOS and Android platforms.
Embedded Face Client component
The Embedded Face Client component creates face templates from face images, as well as provides biometric standards and formats support. The component is designed to run on Android or iOS or ARM Linux devices. The Android devices should be based on at least Snapdragon S4 system-on-chip (Krait 300 processor with 4 cores running at 1.51 GHz). The component extracts a single face template in 1.2 seconds.
The Embedded Face Client component can generalize a face template from several images that include the same face to improve the template's quality. Also, it provides token(1) face images compatible with the Face Image Format as in ISO/IEC 19794 standard. This face image format enables range of applications on variety of devices, including devices that have limited resources required for data storage, and improves recognition accuracy by specifying data format, scene constraints (lighting, pose), photographic properties (positioning, camera focus) and digital image attributes (image resolution, image size).
The following features are provided:
- Face Token Image creation from an image containing human face using eye coordinates which may be either hand marked or detected automatically using Neurotechnology face detection algorithm.
- Face is detected and eye coordinates are acquired using state-of-the-art Neurotechnology face detection and recognition algorithm.
- Geometrical face image normalization according to the proportions and photographic properties, which are specified in ISO/IEC 19794 standard.
- Intelligent image padding algorithm for cutting off parts of Face Token Image as specified in ISO/IEC 19794 standard.
Evaluation of the created token face image for the following quality criteria suggested in ISO/IEC 19794 standard:
- Background uniformity – the background in the token face image should be uniform, not cluttered.
- Sharpness – the token face image should not be blurred.
- Too light or too dark images – the token face image should not be too dark or too light.
- Exposure range of an image – the token face image should have a reasonable exposure range to represent as much details of the subject in the image as possible.
- Evaluation of the token face image quality based on suggestions of ISO/IEC 19794 standard (using the quality criteria above).
Captured faces can be checked for compliancy with ICAO requirements.
These requirements are checked:
- image pixelation, washed out colors;
- face darkness, skin tone, skin reflections, glasses reflections;
- red eyes, looking away eyes (the red eyes can be corrected automatically).
Proprietary algorithms for these functionalities are also included:
- Person's gender recognition.(2)
- Emotions detection: confidence values returned for neutral mood, anger, disgust, fear, happiness, sadness and surprise.(2)
- Facial feature points extraction for each person from an image.
- Age estimation for each person from an image.(2)
- Additional face attributes detection: smile, open-mouth, blink (closed-eyes), glasses, dark-glasses, beard and mustache.(2)
- Live face detection(2) can be used for determining whether a face in a video stream belongs to a real human or is a photo. See recommendations for live face detection for more information.
The Embedded Face Client allows to integrate support for facial image format standards and additional image formats with new or existing biometric systems based on MegaMatcher SDK.
These biometric standards are supported:
- BioAPI 2.0 (ISO/IEC 19784-1:2006) (Framework and Biometric Service Provider for Face Identification Engine)
- CBEFF V1.2 (ANSI INCITS 398-2008) (Common Biometric Exchange Formats Framework)
- CBEFF V2.0 (ISO/IEC 19785-1:2006 with Amd. 1:2010, 19785-3:2007 with Amd. 1:2010) (Common Biometric Exchange Formats Framework)
- CBEFF V3.0 (ISO/IEC 19785-3:2015) (Common Biometric Exchange Formats Framework)
- ISO/IEC 19794-5:2005 (Biometric Data Interchange Formats - Face Image Data)
- ISO/IEC 19794-5:2011 (Biometric Data Interchange Formats - Face Image Data)
- ANSI/INCITS 385-2004 (Face Recognition Format for Data Interchange)
- ANSI/NIST-CSL 1-1993 (Data Format for the Interchange of Fingerprint, Facial, & SMT Information)
- ANSI/NIST-ITL 1a-1997 (Data Format for the Interchange of Fingerprint, Facial, & SMT Information)
- ANSI/NIST-ITL 1-2000 (Data Format for the Interchange of Fingerprint, Facial, & SMT Information)
- ANSI/NIST-ITL 1-2007 (Data Format for the Interchange of Fingerprint, Facial, & Other Biometric Information)
- ANSI/NIST-ITL 1a-2009 (Data Format for the Interchange of Fingerprint, Facial, & Other Biometric Information)
- ANSI/NIST-ITL 1-2011 (Data Format for the Interchange of Fingerprint, Facial, & Other Biometric Information)
- ANSI/NIST-ITL 1-2011 Update:2013 Edition 2 (Data Format for the Interchange of Fingerprint, Facial, & Other Biometric Information)
- ANSI/NIST-ITL 1-2011 Update:2015 (Data Format for the Interchange of Fingerprint, Facial, & Other Biometric Information)
The component also allows to integrate JPEG 2000 with Lossy and Lossless Face Profiles support into applications based on MegaMatcher SDK.
Three licenses for the Embedded Face Client component for each of Android, iOS and ARM Linux platforms are included with MegaMatcher 10.0 Standard SDK and MegaMatcher 10.0 Extended SDK. More licenses for this component can be purchased any time by MegaMatcher 10.0 SDK customers.
- Token in this context is used as "symbolic image, good enough image for machine recognition." Token Image as in ISO/IEC19794-5: "A Face Image Type that specifies frontal images with a specific geometric size and eye positioning based on the width and height of the image. This image type is suitable for minimizing the storage requirements for computer face recognition tasks such as verification while still offering vendor independence and human verification (versus human examination which requires more detail) capabilities."
- Face template should be extracted with the Face Extractor before using these algorithms.