TY - JOUR
T1 - Face recognition using nearest feature space embedding
AU - Chen, Yin Nong
AU - Han, Chin Chuan
AU - Wang, Cheng Tzu
AU - Fan, Kuo Chin
N1 - Funding Information:
The authors thank Professor Stan Z. Li and the anonymous reviewers for providing valuable comments which considerably improved the quality of this paper. The work was supported by the National Science Council under grant no. NSC 97-2221-E-239 -023 -MY2, and by the Technology Development Program for Academia of DOIT, MOEA, Taiwan under grant no. 98-EC-17-A-02-S1-032.
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
KW - Face recognition
KW - Fisher criterion
KW - Laplacianface
KW - nearest feature line
KW - nearest feature space
UR - http://www.scopus.com/inward/record.url?scp=79955423983&partnerID=8YFLogxK
U2 - 10.1109/TPAMI.2010.197
DO - 10.1109/TPAMI.2010.197
M3 - 期刊論文
C2 - 21079273
AN - SCOPUS:79955423983
SN - 0162-8828
VL - 33
SP - 1073
EP - 1086
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
JF - IEEE Transactions on Pattern Analysis and Machine Intelligence
IS - 6
M1 - 5639012
ER -