基于2DLDA与SVM的人脸识别算法

[1] Face recognition by support vector ma-chines. GUO G,,LI S Z,CHAN K. IEEE International Conference on Automatic Face andGesture Recognition . 2000

[2] Fast support vec-tor machine training and classification on graphics processors. CATANZARO B,,SUNDARAMN,KEUTZER K. Proceedings of the 25th International Conference on Machine Learn-ing . 2008

[3] Two-dimensional linear discriminant analysis of principal componentvectors for face recognition. SANQUANASAT P,,ASDORNWISED W,JITAPUNKUL S,et al. IEEE International Conference onAcoustics,Speech and Signal Processing . 2006

[4] Face rec-ognition using lda-based algorithms. LUJ,PLATANIOTIS K N,VENETSANOPULAOS A N. IEEE Transactions on Neu-ral Networks . 2003

[5] Non-iterative two-dimensional linear dis-criminant analysis for face recognition. INOUE K,,URAHAMAK. IPSJSIG Technical Report . 2006

[6] Using support vector machines toenhance the performance of Bayesian face recognition. LI ZHIFENG,TANG XIAOOU. IEEETransactions on Information Forensics and Security . 2007

[7] Frequency-based non-rigid motion analysis. Nastar C,Ayache N. IEEE Transactions on Pattern Analysis and Machine Intelligence . 1996

[8] An introduction to support vector machines and other kernel-based learning methods. Cristianini N,Shawe-Taylor J. . 2000

[9] A novel method for Fisher discriminant analysis. Yong Xu,Jing-Yu Yang,Zhong Jin. Pattern Recognition . 2004

[10] Face recognition using wavelettransform and locally discriminating projection. SUJARITHA M,,ANNADURAI S. InternationalConference on Computational Intelligence and Multimedia Applica-tions . 2007

[1] Face recognition by support vector ma-chines. GUO G,,LI S Z,CHAN K. IEEE International Conference on Automatic Face andGesture Recognition . 2000

[2] Fast support vec-tor machine training and classification on graphics processors. CATANZARO B,,SUNDARAMN,KEUTZER K. Proceedings of the 25th International Conference on Machine Learn-ing . 2008

[3] Two-dimensional linear discriminant analysis of principal componentvectors for face recognition. SANQUANASAT P,,ASDORNWISED W,JITAPUNKUL S,et al. IEEE International Conference onAcoustics,Speech and Signal Processing . 2006

[4] Face rec-ognition using lda-based algorithms. LUJ,PLATANIOTIS K N,VENETSANOPULAOS A N. IEEE Transactions on Neu-ral Networks . 2003

[5] Non-iterative two-dimensional linear dis-criminant analysis for face recognition. INOUE K,,URAHAMAK. IPSJSIG Technical Report . 2006

[6] Using support vector machines toenhance the performance of Bayesian face recognition. LI ZHIFENG,TANG XIAOOU. IEEETransactions on Information Forensics and Security . 2007

[7] Frequency-based non-rigid motion analysis. Nastar C,Ayache N. IEEE Transactions on Pattern Analysis and Machine Intelligence . 1996

[8] An introduction to support vector machines and other kernel-based learning methods. Cristianini N,Shawe-Taylor J. . 2000

[9] A novel method for Fisher discriminant analysis. Yong Xu,Jing-Yu Yang,Zhong Jin. Pattern Recognition . 2004

[10] Face recognition using wavelettransform and locally discriminating projection. SUJARITHA M,,ANNADURAI S. InternationalConference on Computational Intelligence and Multimedia Applica-tions . 2007