Face Verification SDK

Biometric identity verification for large-scale high-security applications

The Face Verification SDK is designed for integration of facial authentication into enterprise and consumer applications for mobile devices and PCs. The simple API of the library component helps to implement solutions like payment, e-services and all other apps that need enhanced security through biometric face recognition, while keeping their overall size small for easy deployment to millions of users.

Different liveness detection functionalities are included to implement anti-spoofing mechanism with the possibility of configuring the balance between security and usability of the application.

Available for new and existing customers of VeriLook SDK and MegaMatcher SDK on Android, iOS, Microsoft Windows, Mac OS X and Linux platforms.

Download Brochure (PDF).

Download demo app for Android.

Download 30-day SDK Trial.

Technical information and specifications

The Face Verification SDK provides certain capabilities for facial recognition applications, including high-level API for all operations and face liveness check. There are also certain requirements for facial image and posture.

General specifications

The following operations are available via the high-level API:

  • Face enrollment into the internal database – an image with a face is captured from a camera, the face template is extracted from the image and saved into the database.
    • Up to 10 records can be stored in the database.
    • Custom metainformation (like person's name) can be provided during calling this operation to store it in the database together with the face template.
  • Face verification against a specific face from the database – an image with a face is captured from a camera, the face template is extracted from the image and matched against the template stored in the specified database record.
    • Integrators can enable or disable face liveness detection to prevent fraud attempts with a photo.
    • Parameters like template size or matching quality threshold can be modified before calling the enrollment or verification operations.
  • Database record removal.

Basic Recommendations for facial image and posture

The face recognition accuracy 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 a 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 the video frames.
  • Several face enrollments 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.
  • The face recognition engine is intended for usage with near-frontal face images and has certain tolerance to face posture:
    • head roll (tilt) – ±15 degrees;
    • head pitch (nod) – ±15 degrees from frontal position.
    • head yaw (bobble) – ±15 degrees from frontal position.

Live Face Detection

A live video stream from a camera is required for face liveness check:

  • 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.
      • 5 frames per second or better frame rate required.
      • This mode can work with both colored and grayscale images.
      • This mode requires the user to perform all requested actions to pass the liveness check.
    • 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 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.
      • 5 frames per second or better frame rate recommended.
      • This mode can work with both colored and grayscale images.
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