Face Recognition Based on Dual-Tree Complex Wavelet Transform under Low Illumination Environments
Deng-Yuan Huang, Shr-Huan Di, Wu-Chih Hu, Yi-Jen Su · 2014
Face recognition is quite challenging especially in a varying illumination scenario. In this paper, an effective method for face recognition based on dual-tree complex wavelet transform under low illumination environments is proposed. To alleviate the effect of lighting change on face image, logarithmic transform is performed, and a wavelet denoising model is then used to remove the low-frequency components in the details of LH, HL, and HH subbands. To enhance facial features, six difference images obtained from the four LL subbands are utilized and averaged to a so-called mean face. Results show that serious cast shadows on face images can be effectively removed such that face recognition rate can be greatly improved. To verify the feasibility of the proposed method, Yale face database B is used. Experimental results show that an overall recognition rate of 96% can be achieved.