A novel adaptive illumination normalization approach is proposed to eliminate the effects caused by illumination variations for face recognition. The proposed method divides an image into blocks and performs discrete ...A novel adaptive illumination normalization approach is proposed to eliminate the effects caused by illumination variations for face recognition. The proposed method divides an image into blocks and performs discrete cosine transform(DCT) in blocks independently in the logarithm domain. For each block-DCT coefficient except the direct current(DC) component, we take the illumination as main signal and take the reflectance as "noise". A data-driven and adaptive soft-thresholding denoising technique is employed in each block-DCT coefficient except the DC component. Illumination is estimated by applying the inverse DCT in the block-DCT coefficients, and the indirectly obtained reflectance can be used in further recognition task. Experimental results show that the proposed approach outperforms other existing methods. Moreover, the proposed method does not need any prior information, and none of the parameters can be determined by experience.展开更多
基金the Natural Science Foundation of Jiangsu Province(No.BK20150784)the Fund of Jiangsu Key Laboratory of Image and Video Understanding for Social Safety(Nanjing University of Science and Technology)(No.30920140122007)
文摘A novel adaptive illumination normalization approach is proposed to eliminate the effects caused by illumination variations for face recognition. The proposed method divides an image into blocks and performs discrete cosine transform(DCT) in blocks independently in the logarithm domain. For each block-DCT coefficient except the direct current(DC) component, we take the illumination as main signal and take the reflectance as "noise". A data-driven and adaptive soft-thresholding denoising technique is employed in each block-DCT coefficient except the DC component. Illumination is estimated by applying the inverse DCT in the block-DCT coefficients, and the indirectly obtained reflectance can be used in further recognition task. Experimental results show that the proposed approach outperforms other existing methods. Moreover, the proposed method does not need any prior information, and none of the parameters can be determined by experience.
