Document Type

Journal Article

Publisher

The Science and Information Organization Inc.

Faculty

Faculty of Health, Engineering and Science

School

School of Engineering

RAS ID

18454

Comments

This article was originally published as: Hassan, A., Hussain, Z. , & Cai-lin, D. (2014). An Information-Theoretic measure for Face Recognition: Comparison with Structural Similarity. International Journal of Advanced Research in Artificial Intelligence, 3(11), 7-13. Original article available here

Abstract

Automatic recognition of people faces is a challenging problem that has received significant attention from signal processing researchers in recent years. This is due to its several applications in different fields, including security and forensic analysis. Despite this attention, face recognition is still one among the most challenging problems. Up to this moment, there is no technique that provides a reliable solution to all situations. In this paper a novel technique for face recognition is presented. This technique, which is called ISSIM, is derived from our recently published information - theoretic similarity measure HSSIM, which was based on joint histogram. Face recognition with ISSIM is still based on joint histogram of a test image and a database images. Performance evaluation was performed on MATLAB using part of the well-known AT&T image database that consists of 49 face images, from which seven subjects are chosen, and for each subject seven views (poses) are chosen with different facial expressions. The goal of this paper is to present a simplified approach for face recognition that may work in real-time environments. Performance of our information - theoretic face recognition method (ISSIM) has been demonstrated experimentally and is shown to outperform the well-known, statistical-based method (SSIM).

DOI

10.14569/IJACSA.20142.031102

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Share

 
COinS