A novel framework based on wavelet transform and principal component for face recognition under varying illumination

One of the most primitive problems in face recognition is illumination variation, which is of significance for image processing and pattern recognition. Existing studies concentrate on the wavelet transform (WT) without elaborately considering high-frequency information and most do not efficiently tackle a computation explosion of facial dimensions in classification. Therefore, a novel framework based on wavelet transform and principal component is proposed to improve the accuracy under illumination variation in this paper. In the proposed framework, low-frequency sub-band (LFSB) and high-frequency sub-band (HFSB) images from wavelet transform are simultaneously enhanced and denoised, unlike previous studies that usually cause loss of details due to less consideration of HFSB. For LFSB image, a multiple scale Retinex-based steering kernel is designed to enhance more details, and then an adaptive strategy of gamma correction is developed to automatically expand gray-dynamic range. For HFSB image, a non-local mean filtering is established to suppress the noise and subsequently, more image details are preserved by local of mean of local variance. Moreover, the principal component technique based Fisherface and virtual auxiliary sample strategy is developed in order to overcome the computation explosion of facial dimensions, in which a sample strategy with interpolation mechanism is employed to avoid the complicated singularity and Fisherface analysis is further applied to extract features and dimensionality reduction. In addition, the particle swarm optimization-neural network (PSO-NN) is employed to perform classification in the framework. Experimental results prove that the proposed framework can effectively obtain the robust visual effect under varying illumination and significantly improve the recognition performance in comparison to existing methods.