TY - GEN
T1 - Gabor feature-based fast neighborhood component analysis for face recognition
AU - Wang, Faqiang
AU - Zhang, Hongzhi
AU - Wang, Kuanquan
AU - Zuo, Wangmeng
PY - 2012
Y1 - 2012
N2 - Subspace methods have been very successful in face recognition. Neighborhood components analysis (NCA), one popular subspace method, however, cannot outperform discriminative common vectors (DCV) when applied to face recognition. In this paper, we proposed a Gabor feature-based fast NCA method (Gabor-FNCA). First, we extract multi-scale and multi-orientation Gabor features for more robust and enhanced face recognition. Then, we claimed that the FNCA learning problem would be ill-posed for high dimensional data dimensionality reduction. To address this problem, we first use principal component analysis (PCA) to transform the data in a low-dimensional subspace, and then use the FNCA model which including a Frobenius norm regularizer to learn the linear projection matrix. Experimental results on the ORL and FERET face datasets shows that the proposed Gabor-FNCA method is effective for face recognition.
AB - Subspace methods have been very successful in face recognition. Neighborhood components analysis (NCA), one popular subspace method, however, cannot outperform discriminative common vectors (DCV) when applied to face recognition. In this paper, we proposed a Gabor feature-based fast NCA method (Gabor-FNCA). First, we extract multi-scale and multi-orientation Gabor features for more robust and enhanced face recognition. Then, we claimed that the FNCA learning problem would be ill-posed for high dimensional data dimensionality reduction. To address this problem, we first use principal component analysis (PCA) to transform the data in a low-dimensional subspace, and then use the FNCA model which including a Frobenius norm regularizer to learn the linear projection matrix. Experimental results on the ORL and FERET face datasets shows that the proposed Gabor-FNCA method is effective for face recognition.
KW - Discriminative common vectors
KW - Face recognition
KW - Metric learning
KW - Neighborhood component analysis
KW - Subspace method
UR - https://www.scopus.com/pages/publications/84865026052
U2 - 10.1007/978-3-642-31576-3_35
DO - 10.1007/978-3-642-31576-3_35
M3 - 会议稿件
AN - SCOPUS:84865026052
SN - 9783642315756
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 266
EP - 273
BT - Intelligent Computing Theories and Applications - 8th International Conference, ICIC 2012, Proceedings
T2 - 8th International Conference on Intelligent Computing Theories and Applications, ICIC 2012
Y2 - 25 July 2012 through 29 July 2012
ER -