Feature Selection and Recognition of Face by using Hybrid Chaotic PSO-BFO and Appearance-Based Recognition Algorithms

Santosh Kumar and Sanjay Kumar Singh
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Santosh Kumar: Department of Computer Science & Engineering, Indian Institute of Technology (BHU), Varanasi, India
Sanjay Kumar Singh: Department of Computer Science & Engineering, Indian Institute of Technology (BHU), Varanasi, India

International Journal of Natural Computing Research (IJNCR), 2015, vol. 5, issue 3, 26-53

Abstract: Swarm intelligence based approaches are a recent optimization algorithm that simulates the groups (collective) behavior of decentralized and self-organized systems and have gained more proliferation due to a variety of applications and uses in the feature selection to solve the complex problems and classify the objects based on chosen optimal set of features. Feature selection is a process that selects a subset from the extracted features sets according to some criterions for optimization. In computer vision based face recognition systems, feature selection, and representation algorithms play an important role for the selection of optimal, and discriminatory sets of facial feature vectors from the face database. This paper presents a novel approach for facial feature selection by using Hybrid Particle Swarm Optimization (PSO), and Bacterial Foraging Optimization (BFO) optimization algorithms. The hybrid approach consists of two parts: (1) two types of chaotic mappings are introduced in different phase of proposed hybrid algorithms which preserve the huge diversity of population and improve the global searching and exploration capability; (2) In proposed hybrid approach, appearance based (holistic) face representation and recognition approaches such as Principal Component Analysis (PCA), Local Discriminant Analysis (LDA), Independent Component Analysis (ICA) and Discrete Cosine Transform (DCT) extract feature vectors from the Yale face database. Then features are selected by applying hybrid Chaotic PSO and BFO algorithms for the selection of optimal set of features; it quickly searches the feature subspace of facial features that is the most beneficial for classification and recognition of individuals. From the experimental results, the authors have compared the performance of proposed hybrid approach with existing approaches and conclude that hybrid approach can be efficiently used for feature selection for classification and recognition of face of individuals.

Date: 2015
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