Investigation of feature dimension reduction based DCT/SVM for face recognition

We examine the problem of how to discriminate between objects of more than two classes using psilaminimum informationpsila. This paper presents an efficient face recognition system, based on discrete cosine transform (DCT) and support vector machines (SVM). The idea is to reduce dimensionality of face space. DCT is used to extract pertinent information which represent low frequency in each block. Then the extracted DCT coefficients are used as features for the classification process, which is performed using SVM. The proposed approach was thoroughly tested, using ORL face databases. The obtained results are very encouraging, outperforming traditional methods like PCA, LDA or DCT based MLP in recognition systems.

[1]  Josef Kittler,et al.  Component-based LDA face description for image retrieval and MPEG-7 standardisation , 2005, Image Vis. Comput..

[2]  Gérard Dreyfus,et al.  Single-layer learning revisited: a stepwise procedure for building and training a neural network , 1989, NATO Neurocomputing.

[3]  N. Ahmed,et al.  Discrete Cosine Transform , 1996 .

[4]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[5]  Marian Stewart Bartlett,et al.  Face recognition by independent component analysis , 2002, IEEE Trans. Neural Networks.

[6]  Meng Joo Er,et al.  High-speed face recognition based on discrete cosine transform and RBF neural networks , 2005, IEEE Transactions on Neural Networks.

[7]  Subhash Kak,et al.  Block-level discrete cosine transform coefficients for autonomic face recognition , 2003 .

[8]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[9]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[10]  Jian-Huang Lai,et al.  Face representation using independent component analysis , 2002, Pattern Recognit..

[11]  Guodong Guo,et al.  Support vector machines for face recognition , 2001, Image Vis. Comput..

[12]  Monson H. Hayes,et al.  A hidden markov model-based approach for face detection and recognition , 1999 .

[13]  Martin D. Levine,et al.  Face Recognition Using the Discrete Cosine Transform , 2001, International Journal of Computer Vision.

[14]  Guodong Guo,et al.  Face recognition by support vector machines , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[15]  Tomaso A. Poggio,et al.  Face recognition: component-based versus global approaches , 2003, Comput. Vis. Image Underst..

[16]  Alex Pentland,et al.  View-based and modular eigenspaces for face recognition , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Kuldip K. Paliwal,et al.  Features for robust face-based identity verification , 2003, Signal Process..

[18]  Hong Yan,et al.  An Analytic-to-Holistic Approach for Face Recognition Based on a Single Frontal View , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Vijayan K. Asari,et al.  An improved face recognition technique based on modular PCA approach , 2004, Pattern Recognit. Lett..

[20]  Rainer Stiefelhagen,et al.  Analysis of Local Appearance-Based Face Recognition: Effects of Feature Selection and Feature Normalization , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[21]  Roberto Brunelli,et al.  Face Recognition: Features Versus Templates , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Matthew Turk,et al.  A Random Walk through Eigenspace , 2001 .