Face Identification based Bio-Inspired Algorithms

Sanaa Ghouzali¹ and Souad Larabi²

¹Department of Information Technology, King Saud University, Saudi Arabia

²Computer Science Department, Prince Sultan University, Saudi Arabia

Abstract: Most biometric identification applications suffer from the curse of dimensionality as the database size becomes very large, which could negatively affect both the identification performance and speed. In this paper, we use Projection Pursuit (PP) methods to determine clusters of individuals. Support Vector Machine (SVM) classifiers are then applied on each cluster of users separately. PP clustering is conducted using Friedman and Kurtosis projection indices optimized by Genetic Algorithm and Particle Swarm Optimization methods. Experimental results obtained using YALE face database showed improvement in the performance and speed of face identification system.

Keywords: Support vector machine, projection pursuit, particle swarm optimization, genetic algorithms, Kurtosis index, Friedman index.