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Face Recognition Using Nearest Feature Space Embedding
June 2011 (vol. 33 no. 6)
pp. 1073-1086
Ying-Nong Chen, National Central University, Jhongli
Chin-Chuan Han, National United University, Miaoli
Cheng-Tzu Wang, National Taipei University of Education, Taipei
Kuo-Chin Fan, National Central University, Jhongli
Face recognition algorithms often have to solve problems such as facial pose, illumination, and expression (PIE). To reduce the impacts, many researchers have been trying to find the best discriminant transformation in eigenspaces, either linear or nonlinear, to obtain better recognition results. Various researchers have also designed novel matching algorithms to reduce the PIE effects. In this study, a nearest feature space embedding (called NFS embedding) algorithm is proposed for face recognition. The distance between a point and the nearest feature line (NFL) or the NFS is embedded in the transformation through the discriminant analysis. Three factors, including class separability, neighborhood structure preservation, and NFS measurement, were considered to find the most effective and discriminating transformation in eigenspaces. The proposed method was evaluated by several benchmark databases and compared with several state-of-the-art algorithms. According to the compared results, the proposed method outperformed the other algorithms.

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Index Terms:
Face recognition, nearest feature line, nearest feature space, Fisher criterion, Laplacianface.
Citation:
Ying-Nong Chen, Chin-Chuan Han, Cheng-Tzu Wang, Kuo-Chin Fan, "Face Recognition Using Nearest Feature Space Embedding," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 6, pp. 1073-1086, June 2011, doi:10.1109/TPAMI.2010.197
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