An extended PCA and LDA for color face recognition

In this paper, we propose a new method for color face recognition. We extract the first, second and third channels of each color image and use PCA and LDA to get a score of each channel, then we use a combine scheme to attain a final score and use this final score to classify test samples. In order to check the performance of our method, we conduct experiments on Georgia Tech (GT) color face database, at the same time, we compare our method with PCA and LDA, and experiment results show that our methods take better performance.

[1]  Kohji Fukunaga,et al.  Introduction to Statistical Pattern Recognition-Second Edition , 1990 .

[2]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[3]  Chengjun Liu,et al.  Color Image Discriminant Models and Algorithms for Face Recognition , 2008, IEEE Transactions on Neural Networks.

[4]  D. Zhang,et al.  Principle Component Analysis , 2004 .

[5]  Yong Man Ro,et al.  Color Local Texture Features for Color Face Recognition , 2012, IEEE Transactions on Image Processing.

[6]  Luis Torres,et al.  The importance of the color information in face recognition , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).

[7]  Terence Sim,et al.  The CMU Pose, Illumination, and Expression Database , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  R. Fisher THE STATISTICAL UTILIZATION OF MULTIPLE MEASUREMENTS , 1938 .

[9]  Pavel Pudil,et al.  Introduction to Statistical Pattern Recognition , 2006 .

[10]  B. K. Julsing,et al.  Face Recognition with Local Binary Patterns , 2012 .

[11]  Georgios S. Paschos,et al.  Perceptually uniform color spaces for color texture analysis: an empirical evaluation , 2001, IEEE Trans. Image Process..

[12]  Zihan Zhou,et al.  Demo: Robust face recognition via sparse representation , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[13]  Berk Gökberk,et al.  Feature selection for pose invariant face recognition , 2002, Object recognition supported by user interaction for service robots.

[14]  Wen Gao,et al.  Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[15]  Avinash C. Kak,et al.  PCA versus LDA , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Hyeonjoon Moon,et al.  The FERET evaluation methodology for face-recognition algorithms , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[17]  Stan Z. Li,et al.  Face recognition using the nearest feature line method , 1999, IEEE Trans. Neural Networks.

[18]  H. Deutsch Principle Component Analysis , 2004 .

[19]  Jagath C. Rajapakse,et al.  Color channel encoding with NMF for face recognition , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[20]  Matti Pietikäinen,et al.  Face Recognition with Local Binary Patterns , 2004, ECCV.

[21]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  J. Wade Davis,et al.  Statistical Pattern Recognition , 2003, Technometrics.

[23]  Sang Uk Lee,et al.  Face recognition using face-ARG matching , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Zhi-Hua Zhou,et al.  Recognizing partially occluded, expression variant faces from single training image per person with SOM and soft k-NN ensemble , 2005, IEEE Transactions on Neural Networks.