文献类型：期刊文献

英文题名：Optimal linear combination of facial regions for improving identification performance

作者：Wong, Kin-Chung[1]; Lin, Wei-Yang[2]; Hu, Yu Hen[1]; Boston, Nigel[1]; Zhang, Xueqin[3]

机构：[1]Univ Wisconsin, Dept Elect & Comp Engn, Madison, WI 53706 USA;[2]Natl Chung Cheng Univ, Dept Comp Sci & Informat Engn, Chiayi 621, Taiwan;[3]E China Univ Sci & Technol, Dept Elect & Commun Engn, Shanghai 200237, Peoples R China

年份：2007

卷号：37

期号：5

起止页码：1138

外文期刊名：IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS

收录：;EI(收录号：20074110863693);WOS:【SCI-EXPANDED(收录号:WOS:000249594500006)】；

语种：英文

外文关键词：face recognition; Face Recognition Grand Challenge (FRGC); information fusion; 3-D faces

摘要：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.