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Recognition of JPEG Compressed Face Images Based on AdaBoost

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This paper presents an advanced face recognition system based on AdaBoost algorithm in the JPEG compressed domain. First, the dimensionality is reduced by truncating some of the block-based DCT coefficients and the nonuniform illumination variations are alleviated by discarding the DC coefficient of each block. Next, an improved AdaBoost.M2 algorithm which uses Euclidean Distance(ED) to eliminate non-effective weak classifiers is proposed to select most discriminative DCT features from the truncated DCT coefficient vectors. At last, the LDA is used as the final classifier. Experiments on Yale face databases show that the proposed approach is superior to other methods in terms of recognition accuracy, efficiency, and illumination robustness. 
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WP5: Detection, Extraction and Annotation of Knowledge. 
School of Informatics University of Bradford  Jianmin Jiang 
2008-01-24 17:07  Request for more detail


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For LIVE project internal research purposes only.  For LIVE project internal development purposes only. 


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Copyright School of Informatics, University of Bradford.  Add to LIVE's Intellectual Property Watch List 
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SAMT 2007, LNCS 4816, pp. 272–275, 2007. 
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