Face Recognition Using RLDA Method Based on Mutated Cuckoo Search Algorithm to Extract Optimal Features

Regularized-LDA (R-LDA) is one of the most successful holistic approaches that is introduced to overcome the “small sample size” (SSS) problem of the LDA method, which is often encountered in Face Recognition (FR) tasks. R-LDA is based on reducing the high variance of principal components of the within-class scatter matrix to optimize the regularized Fisher's criterion. In this article, the authors assume that some of these components do not have significant information and they can be discarded. To this end, the authors propose CS-RLDA that uses a Cuckoo search (CS) algorithm to select the optimal eigenvectors from a within-class matrix. However, the CS algorithm has a slow convergence speed. To deal with this problem, and to create more diversity and better trade-off between exploitation and exploration around the best solutions, the authors have modified the basic cuckoo algorithm by using a mutation operator. The experimental results performed on the ORL and UMIST databases indicate that the proposed method enhances the performance of FR.