VeriLook SDK

Face identification for stand-alone or Web applications

VeriLook facial identification technology is designed for biometric systems developers and integrators. The technology assures system performance and reliability with live face detection, simultaneous multiple face recognition and fast face matching in 1-to-1 and 1-to-many modes.

Available as a software development kit that allows development of stand-alone and Web-based solutions on Microsoft Windows, Linux, Mac OS X, iOS and Android platforms.

Reliability Tests

We present the testing results to show the VeriLook 9.0 algorithm face recognition reliability evaluations. The following public datasets were used:

  • 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. VeriLook 9.0 algorithm reliability in this test is shown on the ROC charts as green curves.
  • Experiment 2 maximized matching speed. VeriLook 9.0 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
VeriLook ROC chart on NIST MEDS II face image dataset
Click to zoom
LFW dataset
VeriLook ROC chart on LFW face image dataset
Click to zoom
MEDS-II dataset
VeriLook ROC chart on NIST MEDS II face image dataset
LFW dataset
VeriLook ROC chart on LFW face image dataset
VeriLook 9.0 algorithm testing results with MEDS-II and LFW datasets
  MEDS-II LFW
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 0.8976 % 1.1440 % 0.7247 % 1.0740 %
FRR at 0.1 % FAR 1.9500 % 3.7190 % 2.5600 % 5.1120 %
FRR at 0.01 % FAR 5.7600 % 10.1600 % 7.2460 % 14.1100 %
FRR at 0.001 % FAR 12.4300 % 16.1500 % 17.0800 % 27.4800 %