International Journal of Modern Education and Computer Science (IJMECS)
IJMECS Vol. 6, No. 7, 8 Jul. 2014
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A Face Recognition System by Embedded Hidden Markov Model and Discriminating Set Approach
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Author(s)
Vitthal Suryakant Phad
1,*
Prakash S. Nalwade
2
Prashant M. Suryavanshi
1
Index Terms
Embedded hidden Markov model (EHMM), Face recognition, Pattern recognition, Discriminating set, Generalization ability
Abstract
Different approaches have been proposed over the last few years for improving holistic methods for face recognition. Some of them include color processing, different face representations and image processing techniques to increase robustness against illumination changes. There has been also some research about the combination of different recognition methods, both at the feature and score levels. Embedded hidden Markov model (E-HHM) has been widely used in pattern recognition. The performance of Face recognition by E-HMM heavily depends on the choice of model parameters. In this paper, we propose a discriminating set of multi E-HMMs based face recognition algorithm. Experimental results illustrate that compared with the conventional HMM based face recognition algorithm the proposed method obtain better recognition accuracies and higher generalization ability.
Cite This Paper
Vitthal Suryakant Phad, Prakash S. Nalwade, Prashant M. Suryavanshi, "A Face Recognition System by Embedded Hidden Markov Model and Discriminating Set Approach", International Journal of Modern Education and Computer Science (IJMECS), vol.6, no.7, pp. 25-30, 2014. DOI:10.5815/ijmecs.2014.07.04
Reference
[1]W. Zhao, R. Chellappa, A. Rosenfeld, P.J. Phillips, Face Recognition: A Literature Survey, ACM Computing Surveys, 2003, pp. 399-458.
[2]Waqas Haider, Hadia Bashir, Abida Sharif, Irfan Sharif and Abdul Wahab, A Survey on Face Detection and Recognition Approaches, Research Journal of Recent Sciences, Vol. 3(4), 56-62, April 2014.
[3]Baum, L. E. & Petrie, T., Statistical inference for probabilistic functions of finite state Markov chains. Annals of Mathematical Statistics, vol. 37, 1966.
[4]Baum, L. E., An inequality and associated maximization technique in statistical estimation for probabilistic functions of Markov processes, Inequalities, vol. 3, pp.1-8, 1972.
[5]F. Samaria, S. Young, An HMM-based architecture for face identification. Image and Computer Vision, 12(8): 537-543, 1994.
[6]Nefian, A.V. & Hayes III, M.H., Face detection and recognition using hidden markov models. Image Processing, ICIP 98, Proceedings. 1998 International Conference on, Vol. 1, pp. 141–145, October 1998.
[7]Nefian, A.V. & Hayes III, M.H., An embedded hmm-based approach for face detection and recognition. Acoustics, Speech, and Signal Processing, ICASSP ’99.Proceedings, IEEE International Conference, 6:3553–3556, March 1999.
[8]Rabiner, L., Juang, B, An introduction to hidden Markov models, IEEE ASSP Magazine, Jan 1986 Volume: 3 Issue: 1 On page(s): 4 - 16 ISSN: 0740-7467.
[9]V. N. Ara and H. H. Monson, “Face recognition using an embedded HMM,” Proc. IEEE Int’l Conf. Audio Video-based Biometric Person Authentication, pp. 19–24(1999).
[10]Muhammad Sharif, Jamal Hussain Shah, Sajjad Mohsin, Mudassar Raza, Sub-Holistic Hidden Markov Model for Face Recognition, Research Journal of Recent Sciences, Vol. 2(5), 10-14, May 2013.
[11]M. Turk and A. Pentland, “Eigenfaces for recognition,” J. Cogn. Neurosci. 3(1), 71–86 (1991).
[12]P.N. Belhumeur, J.P. Hespanha, and D.J. Kriegman, “Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711-720, July 1997.
[13]X. He, S. Yan, Y. Hu, P. Niyogi and H. J. Zhang, “Face recognition using laplacianfaces,” IEEE Trans. Pattern Anal. Mach. Intell. 27(3), 1–13 (2005).
[14]A.V. Nefian and M.H. Hayes III, “Maximum Likelihood Training of the Embedded HMM for Face Detection and Recognition,” Proc. Int’l Conf. Image Processing, vol. 1, pp. 33-36, 2000.(ml hmm).
[15]B.-H. Juang and S. Katagiri, “Discriminative Learning for Minimum Error Classification,” IEEE Trans. Signal Processing, vol. 40, no. 12, pp. 3043-3054, 1992.(mce hmm).
[16]S. Katagiri, B.-H. Juang, and C.-H. Lee, “Pattern Recognition Using a Family of Design Algorithms Based upon the Generalized Probabilistic Descent Method,” Proc. IEEE, vol. 86, no. 11, pp. 2345-2373, 1998.(mce hmm).
[17]Chien, J-T. & Liao, C-P. , Maximum Confidence Hidden Markov Modeling for Face Recognition. Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol. 30, No 4, pp. 606-616, April 2008.
[18]P.J. Phillips, H. Wechsler, J. Huang, and P.J. Rauss, “The FERET Database and Evaluation Procedure for Face-Recognition Algorithms,” Image and Vision Computing, vol. 16, no. 5, pp. 295-306, 1998. [fert dbs).
[19]L. Chen, H. Man, and A.V. Nefian, “Face Recognition Based Multi-Class Mapping of Fisher Scores,” Pattern Recognition, vol. 38,pp. 799-811, 2005.(gtfd dbs).
[20]A.V. Nefian, “Embedded Bayesian Networks for Face Recognition,” Proc. IEEE Int’l Conf. Multimedia and Exp, vol. 2, pp. 133-136, 2002. (gtfd dbs).