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Face recognition (FR) under varying lighting conditions is challenging, and exacting illumination invariant features is an effective approach to solve this problem. In this paper, we propose a novel illumination normalization method called Selective Illumination Enhancement Technique (SIET) wherein the dark regions are selectively illuminated by using a correction factor which is determined by an Energy function. Also, we propose a Threshold based Discrete Wavelet Transform feature extraction for enhancing the performance of the FR system. Individual stages of the FR system are examined and an attempt is made to improve each stage. A Binary Particle Swarm Optimization (BPSO)-based feature selection algorithm is used to search the feature vector space for the optimal feature subset. Experimental results show the promising performance of Threshold based DWT extraction technique on ORL and UMIST databases and SIET on illumination variant databases like Extended Yale B and Color FERET

Face Recognition using Threshold Based DWT Feature Extraction and Selective Illumination Enhancement Technique