Optimal linear combination of facial regions for improving identification performance

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
Kin-Chung WongXueqin Zhang

Abstract

This paper presents a novel 3-D multiregion face recognition algorithm that consists of new geometric summation invariant features and an optimal linear feature fusion method. A summation invariant, which captures local characteristics of a facial surface, is extracted from multiple subregions of a 3-D range image as the discriminative features. Similarity scores between two range images are calculated from the selected subregions. A novel fusion method that is based on a linear discriminant analysis is developed to maximize the verification rate by a weighted combination of these similarity scores. Experiments on the Face Recognition Grand Challenge V2.0 dataset show that this new algorithm improves the recognition performance significantly in the presence of facial expressions.

References

Mar 30, 2005·IEEE Transactions on Pattern Analysis and Machine Intelligence·Kyong I ChangPatrick J Flynn
Jan 1, 1997·IEEE Transactions on Neural Networks·C Ji, S Ma

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Citations

Sep 10, 2010·Brain : a Journal of Neurology·Mahinda YogarajahJohn S Duncan
Nov 4, 2009·IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society·Mingli SongMengchu Zhou
Sep 2, 2010·IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society·Claudio MarroccoFrancesco Tortorella
Nov 2, 2011·IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society·Shaoning PangNikola K Kasabov
May 2, 2012·IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society·Ping-Han LeeYi-Ping Hung

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