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Nearest Intra-Class Space Classifier for Face Recognition

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Nearest Intra-Class Space Classifier for Face Recognition
In this paper, we propose a novel classification method, called nearest intra-class space (NICS), for face recognition. In our method, the distribution of face patterns of each person is represented by the intra-class space to capture all intra-class variations. Then, a regular principal subspace is derived from each intra-class space using principal component analysis. The classification is based on the nearest weighted distance, combining distance-from-subspace and distance-in-subspace,between the query face and each intra-class subspace. Experimental results show that the NICS classifier outperforms other classifiers in terms of recognition performance.
Stan Z. Li, Tieniu Tan, Wei Liu, Yunhong Wang
Added 09 Nov 2009
Updated 09 Nov 2009
Type Conference
Year 2004
Where ICPR
Authors Stan Z. Li, Tieniu Tan, Wei Liu, Yunhong Wang
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