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 on Android, iOS, Microsoft Windows, macOS and Linux platforms.

Download Brochure (PDF).

Download demo app for Android.

Download SDK Trial.

Reliability Tests

We present the testing results to show the Face Verification SDK algorithm reliability evaluations with the following public datasets:

  • NIST Special Database 32 - Multiple Encounter Dataset (MEDS-II).
    • All full-profile face images from the dataset were removed because they are not supported by VeriLook SDK. This resulted in 1,216 images of 518 persons.
  • University of Massachusetts Labeled Faces in the Wild (LFW).
    • According to the original protocol, only 6,000 pairs (3,000 genuine and 3,000 impostor) should be used to report the results. But recent algorithms are "very close to the maximum achievable by a perfect classifier" [source]. Instead, as Neurotechnology algorithms were not trained on any image from this dataset, verification results on matching each pair of all 13,233 face images of 5,729 persons were chosen to be reported.
    • All identity mistakes, which had been mentioned on the LFW website, were fixed. Also, several not mentioned issues were fixed.
    • Some images from the LFW dataset contained multiple faces. The correct faces for assigned identities were chosen manually to solve these ambiguities.

Both datasets contained faces, which are impossible to detect with the fastest near-frontal face detection. Face detection parameters were tuned to fully automatically detect maximum amount of faces with highest recall ratio using ±45° detectors, no speed optimizations, smaller search step and other parameters.

Two experiments were performed with each dataset:

  • Experiment 1 maximized matching accuracy. Face Verification 11.0 SDK algorithm reliability in this test is shown on the ROC charts as blue curves.
  • Experiment 2 maximized matching speed. Face Verification 11.0 SDK algorithm reliability in this test is shown on the ROC charts as red curves.

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). Equal error rate (EER) is the rate at which both FAR and FRR are equal.

MEDS-II dataset
Face Verification SDK ROC chart on NIST MEDS II face image dataset
Click to zoom
LFW dataset
Face Verification SDK ROC chart on LFW face image dataset
Click to zoom
MEDS-II dataset
Face Verification SDK ROC chart on NIST MEDS II face image dataset
LFW dataset
Face Verification SDK ROC chart on LFW face image dataset
Face Verification 11.0 SDK algorithm testing results with MEDS-II and LFW datasets
Exp. 1 Exp. 2 Exp. 1 Exp. 2
Image count 1216 13233
Subject count 518 5729
Session count 1 - 18 1 - 530
Image size (pixels) variable 250 x 250
Template size (bytes) 7128 5066 7128 5066
EER 0.9247 % 1.0550 % 0.6135 % 0.9895 %
FRR at 0.1 % FAR 2.1770 % 3.8100 % 2.2920 % 5.2150 %
FRR at 0.01 % FAR 5.9860 % 10.1100 % 7.5900 % 14.6700 %
FRR at 0.001 % FAR 15.1900 % 16.2400 % 17.9700 % 29.3900 %
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