.. _introduction:

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Introduction 
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|product-name| is a cross-platform library aimed at system integrators willing to quickly add face detection, tracking, analysis, liveness check and recognition capabilities to their products. 
It is available as a Software Development Kit (SDK) offering a comprehensive interface to simplify integration of the library on servers, desktops/laptops, mobile and edge devices.

Technology
==========

Thanks to many years of research and development in the field of computer vision and artificial intelligence, our experts have designed a unique algorithm that reproduces the visual recognition abilities of the human brain.

With the power of deep learning techniques trained on millions of faces, our technology outperforms human performance enabling unconstrained/non-voluntary real-time detection and recognition of faces in a crowd. It operates on any type of people face whatever the gender, age or race and is robust to intra-personal variations such as ageing, facial hair, scars/injuries, accessories (e.g. glasses, hats, etc.), cosmetics, etc. The technology is also robust to variations of the lightning conditions and has the ability to work with a large range of cameras under either visible or near-infrared light.

The face identification process is nearly instantaneous. It has the capability to compare millions of faces in less than one second on a single processing unit. The matching algorithm has also very low resource requirements enabling possible applications on secure elements.

id3 Technologies face recognition algorithm has proven excellent tradeoff between accuracy, speed and template size in the NIST ongoing Face Recognition Vendor Test (FRVT).


Biometric Performance
=====================

The overall accuracy of a facial recognition system may vary according to a number of factors such as:

- Quality of the camera system,
- Lighting conditions,
- Facial pose variations,
- Population under test,
- etc.

Performance metrics
-------------------
False non match rate (FNMR) is the proportion of mated comparisons below a threshold set to achieve the false match rate (FMR) specified. FMR is the proportion of impostor comparisons at or above that threshold. Since FMR and FNMR is in inverse proportion to each other, choosing the operational threshold is a trade-off between system security and user convenience.

NIST FRVT evaluation
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The Face Recognition Vendor Test (FRVT) was initiated by the National Institute of Standards and Technologies (NIST) in February 2017. It is aimed at measurement of the performance of automated face recognition technologies applied to a wide range of civil, law enforcement and homeland security applications including verification of visa images, de-duplication of passports, recognition across photojournalism images, and identification of child exploitation victims.

The latest report of the FRVT can be found [here](https://pages.nist.gov/frvt/reports/11/frvt_11_report.pdf).

The face encoder 9A of this SDK corresponds to the *id3_008* submission.

A complete report card can also be found [here](https://pages.nist.gov/frvt/reportcards/11/id3_008.html), showing among things, the evolution of our face recognition technology over the years.


