Efficient Multispectral Face Recognition using Random Feature Selection and PSO-SVM

Abstract

Biometric technologies have been widely used in recent years for authentication and security purposes. Face recognition is one of the important techniques for its simple way to obtain the subject samples without being intrusive. However, this technology has been employed mostly using the visible spectrum only, which suffers from some limitations such as illumination change, facial expression and pose variations. The infrared spectrum offers some advantages over the visible spectrum, mainly the robustness to light change. In this paper, we propose a new multispectral framework that uses both infrared and visible spectra with an optimization based on a new proposed feature selection algorithm and the PSO-SVM. Experimental tests were conducted on IRIS OTCBVS Thermal/Visible and CSIST Lab 2 Databases. The obtained results clearly demonstrate the effectiveness of our new framework compared to a mono-spectral face recognition system.