Facial Recognition

Introduction

Humans often use faces to recognize individuals, and advancements in computing capability over the past few decades now enable similar recognitions automatically. Early facial recognition algorithms used simple geometric models, but the recognition process has now matured into a science of sophisticated mathematical representations and matching processes. Major advancements and initiatives in the past 10 to 15 years have propelled facial recognition technology into the spotlight. Facial recognition can be used for both verification and identification (open-set and closed-set).

History

Automated facial recognition is a relatively new concept. Developed in the 1960s, the first semi-automated system for facial recognition required the administrator to locate features (such as eyes, ears, nose, and mouth) on photographs before it calculated distances and ratios to a common reference point, which were then compared to reference data. Click here for more information.

Predominant Approaches

There are two predominant approaches to the facial recognition problem: geometric (feature based) and photometric (view based). Click here for more information.

Standards Overview

Standardization is a vital portion of the advancement of the market and state-of-the-art. Much work is being done at the national and international standard organization levels to facilitate the interoperability and data interchange formats, which will help facilitate technology improvement on a standardized platform. Click here for more information.

Summary

The computer-based facial recognition industry has made many useful advancements in the past decade; however, the need for higher accuracy remains. Through the determination and commitment of industry, government evaluations, and organized standards bodies, growth and progress will continue, raising the bar for face-recognition technology.

* Excerpted from biometrics.gov.