DEVELOP PRODUCTS
WITH FINDFACE SDK

FUNCTIONALITY

The FindFace SDK is a C library that provides access to the cutting-edge face recognition technology based on neural networks. The SDK allows you to quickly and accurately solve the 3 key tasks of face recognition:

Face detection

Finds face fragments in an image and returns a bounding box and control points (eyes, nose, corners of the mouth) for each fragment.

See examples

Biometric sample extraction

500 ms is the time needed to extract a face biometric sample and save it in a temporary binary format. The sample can be later saved to a database and used for face verification.

See examples

Verification

It takes ~75 ns to compare 2 biometric samples and estimate the probability of their belonging to the same person.

See examples

Liveness check

Distinguish a live face in front of a camera from a photo on paper or mobile device screen.

Face attributes extraction

Recognize age, gender, emotions, glasses, beard, and other attributes.

ADVANTAGES

IARPA FRPC winner, in the top among all NIST benchmarks

Biometric sample extraction ~25 faces per second on CPU, or ~240 faces per second on GPU; search/verification ~13.4 mln faces per second. GPU-acceleration support: ~10 times quicker compared to CPU.

Simple integration with your product

Identify and verify faces without being connected to the internet

Liveness detection: protect your system from photo-attacks and spoofing.

Estimate age, emotion, gender, image quality, blur, face angle, presence of a beard and glasses for each face

Two neural networks for your choice: prioritize performance or accuracy.

Phenomenal data processing speed

GPU acceleration support

SYSTEM REQUIREMENTS

CPU/GPU

CPU with AVX support/NVidia video card with 6 Gb of video memory for more accurate calculations, 3 Gb of video memory for quicker calculations

RAM

~3.5 GB RAM

Windows 7, 8, 10 (x64), Linux Ubuntu (x64)

TYPICAL CASES

E-GATE

AUTHENTICATION

ACCESS CONTROL

WEARABLES

GET FINDFACE SDK TRIAL VERSION

Fill out the form and get 14-day free access to the library

get trial

DETECTOR OPERATION EXAMPLES

Detecting a face means locating it in a digital image. Even though a face is small, semi closed, blurred or turned away from the camera, a good detector will find it.

Detection examples from NtechLab:

FACE DETECTION
AT A DISTANCE

SEMI CLOSED FACE
DETECTION

LARGE ANGLES

FACE DETECTION
UNDER COMPLEX CONDITIONS (POOR LIGHTING, BLUR)

KEY FEATURES

Highest accuracy even with the factors that change appearance:

POOR LIGHTING

COVERED HEAD (hat, scarf, shawl)

GLASSES, BEARD, MUSTACHE

SEMI CLOSED FACE

AGE (photo taken 5 years ago)

VARIABILITY OF FACE EXPRESSIONS (smiling/gloomy)

DIVERSE FACE ANGLES

DIFFERENT CONDITIONS (sober/drunk)

DIFFERENT SIZE IN PIXELS

Biometric sample extraction and verification

Biometric sample extraction is a process of obtaining the distinctive features of a particular face from an image using neural networks. The result of this process is a unique array of numbers that describes a face, known as biometric sample, feature vector or face descriptor. It can be further compared to another array of numbers to determine the degree of similarity between the two faces. Look at the real verification cases and acknowledge the algorithm error-free operation in non-ideal conditions with various appearance changes.

FACE FROM SURVEILLANCE CAMERA

KNOWN FACE

FACE FROM SURVEILLANCE CAMERA

KNOWN FACE

FACE FROM SURVEILLANCE CAMERA

KNOWN FACE

Half-covered face