Hybridization of Biogeography-Based Optimization and Gravitational Search Algorithm for Efficient Face Recognition

Hybridization of Biogeography-Based Optimization and Gravitational Search Algorithm for Efficient Face Recognition

Lavika Goel (Birla Institute of Technology and Science (BITS), India), Lavanya B. (Birla Institute of Technology and Science (BITS), India) and Pallavi Panchal (Birla Institute of Technology and Science (BITS), India)
Copyright: © 2019 |Pages: 22
DOI: 10.4018/978-1-5225-7338-8.ch012

Abstract

This chapter aims to apply a novel hybridized evolutionary algorithm to the application of face recognition. Biogeography-based optimization (BBO) has some element of randomness to it that apart from improving the feasibility of a solution could reduce it as well. In order to overcome this drawback, this chapter proposes a hybridization of BBO with gravitational search algorithm (GSA), another nature-inspired algorithm, by incorporating certain knowledge into BBO instead of the randomness. The migration procedure of BBO that migrates SIVs between solutions is done between solutions only if the migration would lead to the betterment of a solution. BBO-GSA algorithm is applied to face recognition with the LFW (labelled faces in the wild) and ORL datasets in order to test its efficiency. Experimental results show that the proposed BBO-GSA algorithm outperforms or is on par with some of the nature-inspired techniques that have been applied to face recognition so far by achieving a recognition rate of 80% with the LFW dataset and 99.75% with the ORL dataset.
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Background

The problem of face recognition has been dealt with by several nature inspired algorithms by employing varying techniques and mechanisms.

Key Terms in this Chapter

Nature Inspired: Nature has inspired many researchers in many ways and thus is a rich source of inspiration. Nowadays, most new algorithms are nature-inspired, because they have been developed by drawing inspiration from nature.

Evolutionary: Evolution is change in the heritable characteristics of biological populations over successive generations.

Recognition Rate: Recognition rate is the total number of correctly identified probe images divided by the total number of probe images.

Hybridization: Hybridization may refer to the process of combining different varieties of organisms to create a hybrid which is better than the parents.

Gravitational Search Algorithm: Gravitational search algorithm is an optimization algorithm based on the law of gravity and mass interactions.

Principal Component Analysis: Principal component analysis is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components.

Biogeography-Based Optimization: BBO is an evolutionary algorithm that optimizes a function by stochastically and iteratively improving candidate solutions with regard to a given measure of quality.

Face Recognition: A facial recognition system is a computer application capable of identifying or verifying a person from a digital image or a video frame from a video source.

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