Adaptive illumination normalization approach based on denoising technique for face recognition

详细信息    查看全文

• 作者：Zhichao Lian 练智超 ; Jie Song 宋 杰 ; Yang Li 李 杨
• 关键词：Key wordsface recognition ; illumination variation ; soft ; thresholding denoising
• 刊名：Journal of Shanghai Jiaotong University (Science)
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：22
• 期：1
• 页码：45-49
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Electrical Engineering; Materials Science, general; Computer Science, general; Architecture, general; Life Sciences, general;
• 出版者：Shanghai Jiaotong University Press
• ISSN：1995-8188
• 卷排序：22

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.