Facial Recognition System

Facial Recognition System

Facial recognition is a way of identifying or confirming an individual’s identity using their face. Facial recognition systems can be used to identify people in photos, videos, or in real-time.
Facial recognition is a category of biometric security. Other forms of biometric software include voice recognition, fingerprint recognition, and eye retina or iris recognition. The technology is mostly used for security and law enforcement, though there is increasing interest in other areas of use.


Many people are familiar with face recognition technology through the FaceID used to unlock iPhones (however, this is only one application of face recognition). Typically, facial recognition does not rely on a massive database of photos to determine an individual’s identity — it simply identifies and recognizes one person as the sole owner of the device, while limiting access to others. Beyond unlocking phones, facial recognition works by matching the faces of people walking past special cameras, to images of people on a watch list. The watch lists can contain pictures of anyone, including people who are not suspected of any wrong doing, and the images can come from anywhere — even from our social media accounts. Facial technology systems can vary, but in general, they tend to operate as follows


Face detection

The camera detects and locates the image of a face, either alone or in a crowd. The image may show the person looking straight ahead or in profile.


Face analysis

Next, an image of the face is captured and analyzed. Most facial recognition technology

Step:3 Converting

the image to data

The face capture process transforms analog information (a face) into a set of digital information (data)


Finding a match

our faceprint is then compared against a database of other known faces.


EnR’s facial recognition software searches an existing database of faces and compares them with the faces detected in the scene to find a match. Face Recognition detects faces in the camera’s field of view – as many as 15 at the same time – and matches them against faces previously stored in the database. Anti-spoofing is provided through “liveness” testing without the need for a stereo or 3D camera. Faces can be enrolled in the database from existing still images or from the video camera itself.<br>

  • Key Features of EnR Facial Recognition Software
  • Facial recognition accuracy over 99.5% on public standard data sets
  • Face recognition in real time, depending on resources
  • Easily enroll faces from still or video images
  • Zero gender or racial bias through model training with millions of faces from datasets from around the world
  • Anti-spoofing technology ensures the system cannot be fooled by a photo or video image
  • Detect matches with faces in the database and provide alerts
  • Create a log of faces in the scene for later investigation
  • REST API for building into applications and devices
  • Search for similar faces from a single camera or across multiple cameras
  • In use in thousands of cameras for access control, VIP greeting, shoplifter and unwanted person applications.


High Accuracy

Using AI and deep learning, EnR’s face recognition has achieved accuracy benchmarks better than industry leaders like Google and Facebook. It scores the following accuracy in the leading public test databases – LFW: 99.6%, YouTube Faces: 96.5%, MegaFace (with 1000 people/distracters): 95.6%.

Real-Time Recognition

Recognition is available in both real-time and off-line modes and enrollment is available from both video and still images. Facial recognition is achieved by analyzing multiple images per face and can be achieved in around 0.5 seconds depending on resources.

Available with REST API/SDK

Face Recognizer is available with a REST API/SDK for OEM partners and application builders. Easy integration of alerts is achieved through http/JSON and an open architecture.

X Platform Availability

Runs on Linux or Windows for server-based deployment, 1-16 cameras per PC based on CPU capacity. In-camera/embedded, facial recognition software works with various camera platforms and popular chipsets running Linux. On-cloud it is available as a Web Server Application and through Cloud Web Services API Interface.

Why EnR’s Face Recognition?

Fast, AI-based Face Recognition

Perfect for access control applications either in-camera or on-server. Identify persons of interest and unwanted persons.

Anti-Spoofing and No Gender/Race Bias

Liveness testing with 2D cameras, and models developed with multi-ethnic and gender datasets to ensure no built-in bias

Highly Accurate Facial Recognition

Very high accuracy in tests with YouTube, LFW, Megaface. Tunable confidence level and continuous training