Recognition of JPEG compressed face images based on AdaBoost

Authors:
Chunmei Qing;Jianming Jiang
Affiliations:
School of Informatics, University of Bradford, UK;School of Informatics, University of Bradford, UK
Venue:
SAMT'07 Proceedings of the semantic and digital media technologies 2nd international conference on Semantic Multimedia
Year:
2007

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Abstract

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.