Bayesian Face Recognition Based on Markov Random Field Modeling

In this paper, a Bayesian method for face recognition is proposed based on Markov Random Fields (MRF) modeling. Constraints on image features as well as contextual relationships between them are explored and encoded into a cost function derived based on a statistical model of MRF. Gabor wavelet coefficients are used as the base features, and relationships between Gabor features at different pixel locations are used to provide higher order contextual constraints. The posterior probability of matching configuration is derived based on MRF modeling. Local search and discriminate analysis are used to evaluate local matches, and a contextual constraint is applied to evaluate mutual matches between local matches. The proposed MRF method provides a new perspective for modeling the face recognition problem. Experiments demonstrate promising results.

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

[2]  Donald Geman,et al.  Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images , 1984 .

[3]  Jieping Ye,et al.  Efficient model selection for regularized linear discriminant analysis , 2006, CIKM '06.

[4]  R. Chellappa,et al.  Digital image restoration using spatial interaction models , 1982 .

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

[6]  Anil K. Jain,et al.  Face detection and synthesis using Markov random field models , 2002, Object recognition supported by user interaction for service robots.

[7]  Patrick J. Flynn,et al.  Overview of the face recognition grand challenge , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[8]  Anil K. Jain,et al.  Markov face models , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[9]  J. M. Hammersley,et al.  Markov fields on finite graphs and lattices , 1971 .

[10]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Hyeonjoon Moon,et al.  The FERET Evaluation Methodology for Face-Recognition Algorithms , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Vladimir Pavlovic,et al.  A hybrid face recognition method using Markov random fields , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[13]  James W. Modestino,et al.  A Markov Random Field Model-Based Approach To Image Interpretation , 1989, Other Conferences.

[14]  Donald Geman,et al.  Bayes Smoothing Algorithms for Segmentation of Binary Images Modeled by Markov Random Fields , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Anil K. Jain,et al.  Markov Random Field Texture Models , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  J. Besag.  Spatial Interaction and the Statistical Analysis of Lattice Systems , 1974 .

[17]  Thomas S. Huang,et al.  Object detection using hierarchical MRF and MAP estimation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.