Analysis and Implementation of Optimization Techniques for Facial Recognition

Abstract

Amidst the wide spectrum of recognition methods proposed, there is still the challenge of these algorithms not yielding optimal accuracy against illumination, pose, and facial expression. In recent years, considerable attention has been on the use of swarm intelligence methods to help resolve some of these persistent issues. In this study, the principal component analysis (PCA) method with the inherent property of dimensionality reduction was adopted for feature selection. The resultant features were optimized using the particle swarm optimization (PSO) algorithm. For the purpose of performance comparison, the resultant features were also optimized with the genetic algorithm (GA) and the artificial bee colony (ABC). The optimized features were used for the recognition using Euclidean distance (EUD), K-nearest neighbor (KNN), and the support vector machine (SVM) as classifiers. Experimental results of these hybrid models on the ORL dataset reveal an accuracy of 99.25% for PSO and KNN, followed by ABC with 93.72% and GA with 87.50%. On the central, an experimentation of the PSO, GA, and ABC on the YaleB dataset results in 100% accuracy demonstrating their efficiencies over the state-of-the art methods.