Face Recognition Using Active Illumination Equalization and Mirror Image Superposition as Pre-processing Techniques

Face Recognition (FR) under varying lighting conditions and pose is very challenging. This paper proposes a novel approach for enhancing the performance of a FR system, employing a unique combination of Active Illumination Equalization (AIE), Image Sharpening (IS), Standard Deviation Filtering (SDF), Mirror Image Superposition (MIS) and Binary Particle Swarm Optimization (BPSO). AIE is used for removal of non-uniform illumination and MIS is used to neutralize pose variance. Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) are used for efficient feature extraction and BPSO-based feature selection algorithm is used to search the feature space for the optimal feature subset. Experimental results, obtained by applying the proposed algorithm on Color FERET, Pointing Head Pose and Extended Yale B face databases, show that the proposed system outperforms other FR systems.