Markov face models

The spatial distribution of gray level intensities in an image can be naturally modeled using Markov random field (MRF) models. We develop and investigate the performance of face detection algorithms derived from MRF considerations. For enhanced detection, the MRF models are defined for every permutation of site indices (pixels) in the image. We find the optimal permutation that provides maximum discriminatory power to identify faces from nonfaces. The methodology presented here is a generalization of the face detection algorithm described previously where a most discriminating Markov chain model was used. The MRF models successfully detect faces in a number of test images.

[1]  Hong Yan,et al.  Locating and extracting the eye in human face images , 1996, Pattern Recognit..

[2]  Dario Maio,et al.  Real-time face location on gray-scale static images , 2000, Pattern Recognition.

[3]  Norbert Krüger,et al.  Face Recognition by Elastic Bunch Graph Matching , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Anil K. Jain,et al.  Face detection in color images , 2002, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[5]  Xavier Guyon,et al.  Random fields on a network , 1995 .

[6]  H. B. Mitchell.  Markov Random Fields , 1982 .

[7]  Emile H. L. Aarts,et al.  Simulated annealing and Boltzmann machines - a stochastic approach to combinatorial optimization and neural computing , 1990, Wiley-Interscience series in discrete mathematics and optimization.

[8]  Penio S. Penev,et al.  Local feature analysis: A general statistical theory for object representation , 1996 .

[9]  Norbert Krüger,et al.  Face Recognition by Elastic Bunch Graph Matching , 1997, CAIP.

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

[11]  Rama Chellappa,et al.  Human and machine recognition of faces: a survey , 1995, Proc. IEEE.

[12]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[13]  Anil K. Jain,et al.  Markov random fields : theory and application , 1993 .

[14]  Michael S. Lew,et al.  Information theory and face detection , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[15]  Thomas S. Huang,et al.  Face detection with information-based maximum discrimination , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[16]  Tomaso A. Poggio,et al.  Example-Based Learning for View-Based Human Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

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

[18]  Anil K. Jain,et al.  Learning-Based Detection, Segmentation, and Matching of Objects , 2001, ICAPR.

[19]  Takeo Kanade,et al.  Neural Network-Based Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  George Casella,et al.  Functional Compatibility, Markov Chains and Gibbs Sampling with Improper Posteriors , 1998 .

[21]  Federico Girosi,et al.  Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[22]  D. Geman,et al.  Efficient Focusing and Face Detection , 1998 .

[23]  Vicki Bruce,et al.  Face Recognition: From Theory to Applications , 1999 .