A novel adaptive crossover bacterial foraging optimization algorithm for linear discriminant analysis based face recognition

• Our evolutionary face recognition algorithm provides improved recognition rate.
• A novel adaptive crossover bacterial foraging optimization algorithm is proposed.
• The new algorithm improves the objective function values.
• Optimal dimension reduction is achieved using ACBFO-Fisher algorithm.
• ACBFO-Fisher algorithm search optimal eigenvectors to improve accuracy.

This paper presents a modified bacterial foraging optimization algorithm called adaptive crossover bacterial foraging optimization algorithm (ACBFOA), which incorporates adaptive chemotaxis and also inherits the crossover mechanism of genetic algorithm. First part of the research work aims at improvising evaluation of the optimal objective function values. The idea of using adaptive chemotaxis is to make it computationally efficient and crossover technique is to search nearby locations by offspring bacteria. Four different benchmark functions are considered for performance evaluation. The purpose of this research work is also to investigate a face recognition algorithm with improved recognition rate. In this connection, we propose a new algorithm called ACBFO-Fisher. The proposed ACBFOA is used for finding optimal principal components for dimension reduction in linear discriminant analysis (LDA) based face recognition. Three well-known face databases, FERET, YALE and UMIST, are considered for validation. A comparison with the results of earlier methods is presented to reveal the effectiveness of the proposed ACBFO-Fisher algorithm.

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