Color face recognition: A multilinear-PCA approach combined with Hidden Markov Models

Hidden Markov Models (HMMs) have been successfully applied to the face recognition problem. However, existing HMM-based techniques use feature (observation) vectors that are extracted only from the images' luminance component, while it is known that color provides significant information. In contrast to the classical PCA approach, Multilinear PCA (MPCA) seems to be an appropriate scheme for dimensionality reduction and feature extraction from color images, handling the color channels in a natural, “holistic” manner. In this paper, we propose an MPCA-based approach for color face recognition, that exploits the strengths of HMMs as classifiers. The proposed methodology was tested on three publicly available color databases and produced high recognition rates, compared to existing HMM-based methodologies.

[1]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[2]  Jiebo Luo,et al.  Special Issue on Image Understanding for Digital Photographs , 2005, Pattern Recognit..

[3]  L. Baum,et al.  An inequality and associated maximization technique in statistical estimation of probabilistic functions of a Markov process , 1972 .

[4]  Jieping Ye,et al.  Generalized Low Rank Approximations of Matrices , 2005, Machine Learning.

[5]  Haiping Lu,et al.  MPCA: Multilinear Principal Component Analysis of Tensor Objects , 2008, IEEE Transactions on Neural Networks.

[6]  Rama Chellappa,et al.  Discriminant Analysis for Recognition of Human Face Images (Invited Paper) , 1997, AVBPA.

[7]  Stephen J. Roberts,et al.  Ensemble hidden Markov models for biosignal analysis , 2002, 2002 14th International Conference on Digital Signal Processing Proceedings. DSP 2002 (Cat. No.02TH8628).

[8]  Monson H. Hayes,et al.  Hidden Markov models for face recognition , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[9]  Rabia Jafri,et al.  A Survey of Face Recognition Techniques , 2009, J. Inf. Process. Syst..

[10]  Chin-Hui Lee,et al.  Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains , 1994, IEEE Trans. Speech Audio Process..

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

[12]  Remco C. Veltkamp,et al.  A Survey of 3D Face Recognition Methods , 2005, AVBPA.

[13]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[14]  Monson H. Hayes,et al.  Face detection and recognition using hidden Markov models , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[15]  Hong Man,et al.  Face recognition based on multi-class mapping of Fisher scores , 2005, Pattern Recognit..

[16]  Department of Electrical,et al.  Computational and Performance Aspects of PCA-Based Face-Recognition Algorithms , 2001, Perception.

[17]  M. Sasikumar,et al.  Feature based and PCA based Face Recognition , 2008, Artificial Intelligence and Pattern Recognition.

[18]  Kin-Man Lam,et al.  Face recognition under varying illumination based on a 2D face shape model , 2005, Pattern Recognit..

[19]  Ara V. Nefian,et al.  Embedded Bayesian networks for face recognition , 2002, Proceedings. IEEE International Conference on Multimedia and Expo.

[20]  Monson H. Hayes,et al.  Maximum likelihood training of the embedded HMM for face detection and recognition , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[21]  Ferdinando Silvestro Samaria,et al.  Face recognition using Hidden Markov Models , 1995 .