Analysis and Implementation of Optimization Techniques for Facial Recognition
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Date
2021
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Hindawi
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. +e 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). +e 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.
Description
Research Article