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Face Recognition Using Local Statistics of Gradients and Correlations  

Ju, Yingai (School of Electronics Engineering, College of IT Engineering, Kyungpook National University)
So, Hyun-Joo (School of Electronics Engineering, College of IT Engineering, Kyungpook National University)
Kim, Nam-Chul (School of Electronics Engineering, College of IT Engineering, Kyungpook National University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.48, no.3, 2011 , pp. 19-29 More about this Journal
Abstract
Until now, many face recognition methods have been proposed, most of them use a 1-dimensional feature vector which is vectorized the input image without feature extraction process or input image itself is used as a feature matrix. It is known that the face recognition methods using raw image yield deteriorated performance in databases whose have severe illumination changes. In this paper, we propose a face recognition method using local statistics of gradients and correlations which are good for illumination changes. BDIP (block difference of inverse probabilities) is chosen as a local statistics of gradients and two types of BVLC (block variation of local correlation coefficients) is chosen as local statistics of correlations. When a input image enters the system, it extracts the BDIP, BVLC1 and BVLC2 feature images, fuses them, obtaining feature matrix by $(2D)^2$ PCA transformation, and classifies it with training feature matrix by nearest classifier. From experiment results of four face databases, FERET, Weizmann, Yale B, Yale, we can see that the proposed method is more reliable than other six methods in lighting and facial expression.
Keywords
face recognition; local gradient; local correlation; $(2D)^2$ PCA;
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