Computer vision is concerned with the automatic extraction, analysis and understanding of useful information from a single image or a sequence of images. It involves the development of a theoretical and algorithmic basis to achieve automatic visual understanding.

Face recognition is the process of identifying one or more people in images or videos by analyzing and comparing patterns. Algorithms for face recognition typically extract facial features and compare them to a database to find the best match. Face recognition is an important part of many biometric, security, and surveillance systems, as well as image and video indexing systems.

Face recognition uses the spatial geometry of distinguishing features of the face. It is a form of computer vision that uses the face to identify or to authenticate a person.

Face recognition leverages computer vision to extract discriminative information from facial images, and pattern recognition or machine learning techniques to model the appearance of faces and to classify them. You can use computer vision techniques to perform feature extraction to encode the discriminative information required for face recognition as a compact feature vector using techniques and algorithms such as:

  • Dense local feature extraction with SURF, BRISK or FREAK descriptors
  • Histogram of oriented gradients
  • Distance between detected facial landmarks such as eyes, noses, and lips

An important difference with other biometric solutions is that faces can be captured from some distance away, with for example surveillance cameras. Therefore face recognition can be applied without the subject knowing that he is being observed. This makes face recognition suitable for finding missing children or tracking down fugitive criminals using surveillance cameras.

Axon realized next in terms of Facial recognition:

  • Mobile. real time alerting and intelligence on demand
  • Scalable. enterprise capability across unlimited locations
  • Accurate. the industry’s most accurate face detection
  • Private. designed to protect personal privacy

How does biometrics compare to other access authentication technologies?

The obvious advantage of biometric technology compared to more conventional or traditional authentication methods, such as personal ID cards, magnetic cards, keys or passwords, is that it is intrinsically linked to an individual person and therefore not easily compromised through theft, collusion or loss.

Most biometric systems are easy to use and this simplifies user management resulting in cost savings to the relevant supplier or industry. Users do not need to remember passwords or PIN numbers and user accounts cannot be shared. If improved reliability or security is needed, it is possible to use a combination of one or more biometric technologies such as facial and voice recognition.