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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Fast Face Token Image component

The Fast Face Token Image component is designed to provide 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 component is intended to be used in high-volume server applications, which run on server hardware with at least Intel Xeon Gold 6126 (2.6 GHz) processor.

The Fast Face Token Image component has the following features:

  • 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 among the others:
    • 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).

The Fast Face Token Image component also includes proprietary algorithms for this functionality:

  • 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 component can be used from C/C++, C# and Java applications on all supported platforms. .NET wrappers of Windows libraries are provided for .NET developers.

One Fast Face Token Image license is 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.


  1. 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."
  2. Face template should be extracted with the Fast Face Extractor before using these algorithms.

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