Image Quality-Based Illumination-Invariant Face Recognition

Abstract

Quality of biometric samples has a significant impact on the accuracy of a biometric recognition system. Various quality factors, such as different lighting conditions, occlusion, and variations in pose and expression may affect an automated face recognition system. One of the most challenging issues in automated face recognition is intra-class variations introduced by the varied facial quality due to the variation in illumination conditions. In this paper, we proposed an adaptive discrete wavelet transform (DWT) based face recognition approach which will normalize the illumination distortion using quality-based normalization approaches. The DWT based approach is used to extract the low and high frequency sub-bands for representing the facial features. In the proposed method, a weighted fusion of the low and high frequency sub-bands is computed to improve the identification accuracy under varying lighting conditions. The selection of fusion parameters is made using fuzzy membership functions. The performance of the proposed method was validated on the Extended Yale Database B. Experimental result shows that the proposed method outperforms some well-known face recognition approaches.