Adaptive Face Recognition Based on Image Quality

Quality of facial images has a great impact on the accuracy of the automated face recognition system. The intraclass variations introduced by varied facial quality due to variation in illumination conditions may degrade the performance of a face recognition system significantly. In this paper, we proposed an adaptive discrete wavelet transform (DWT) based face recognition approach which will normalize the illumination distortion using regional contrast limited adaptive histogram equalization (CLAHE) and discrete cosine transform (DCT) normalization based on the illumination quality. The DWT based approach is used to extract the low and high frequency facial features at different scales. In the proposed method, a weighted fusion of the low and high frequency subbands 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 results depict that the proposed method outperforms some well known face recognition approaches.