A robust automatic face recognition system for real‐time personal identification

Abstract

Purpose

Aims to develop an efficient and robust system for real‐time personal identification by automatic face recognition.

Design/methodology/approach

A wavelet‐based image hierarchy and a guided coarse‐to‐fine search scheme are introduced to improve the computation efficiency in the face detection task. In addition, a Gabor‐based low feature dimensional pattern is proposed to deal with the face recognition problem.

Findings

The proposal of a wavelet‐based image hierarchy and a guided coarse‐to‐fine search scheme is effective to improve the computation efficiency in the face detection task. The introduction of a low feature dimensional pattern is powerful to cope with the transformed appearance‐based face recognition problem. In addition, the use of aggregated Gabor filter responses to represent face images provides a better solution to face feature extraction.

Research limitations/implications

Provides guidance in the design of automatic face recognition system for real‐time personal identification.

Practical implications

Biometrics recognition has been emerging as a new and effective identification technology that attains certain level of maturity. Among many body characteristics that have been used, face is one of the most commonly used characteristics and has drawn considerably large attentions. An automated system to confirm an individual's identity employing features of face is very attractive in many specialized fields.

Originality/value

Introduces a wavelet‐based image hierarchy and a guided coarse‐to‐fine search scheme to improve the computation efficiency in the face detection task. Introduces a Gabor‐based low feature dimensional pattern to deal with the face recognition problem.

Citation

Li, Q., Hong Cheung, K., You, J., Tong, R. and Mak, A. (2006), "A robust automatic face recognition system for real‐time personal identification", Sensor Review, Vol. 26 No. 1, pp. 38-44. https://doi.org/10.1108/02602280610640661