Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

This is an Open Access publication published under CSC-OpenAccess Policy.
Publications from CSC-OpenAccess Library are being accessed from over 74 countries worldwide.
One-Sample Face Recognition Using HMM Model of Fiducial Areas
OJO, John Adedapo, Adeniran, Solomon A.
Pages - 58 - 68     |    Revised - 31-03-2011     |    Published - 04-04-2011
Volume - 5   Issue - 1    |    Publication Date - March / April 2011  Table of Contents
Hidden Markov Model (HMM), Recognition Rate (RR), False Acceptance Rate (FAR), Face Recognition (FR)
In most real world applications, multiple image samples of individuals are not easy to collate for direct implementation of recognition or verification systems. Therefore there is a need to perform these tasks even if only one training sample per person is available. This paper describes an effective algorithm for recognition and verification with one sample image per class. It uses two dimensional discrete wavelet transform (2D DWT) to extract features from images and hidden Markov model (HMM) was used for training, recognition and classification. It was tested with a subset of the AT&T database and up to 90% correct classification (Hit) and false acceptance rate (FAR) of 0.02% was achieved.
CITED BY (4)  
1 Andriady, A., Sanjaya, F., & Alamsyah, D. Pengenalan Wajah Manusia dengan Hidden Markov Model (HMM) dan Fast Fourier Transform (FFT).
2 Babatunde, R. S., Olabiyisi, S. O., Omidiora, E. O., & Ganiyu, R. A. (2015). Local Binary Pattern And Ant Colony Optimization Based Feature Dimensionality Reduction Technique For Face Recognition System. British Journal of Computer Science and Mathematics (Article in Press).
3 Anand, C., & Lawrance, R. (2013). Seven State HMM-Based Face Recognition System along with SVD Coefficients. Biometrics and Bioinformatics, 5(6), 226-233.
4 George, J. P. (2012). development of efficient biometric recognition algorithms based on fingerprint and face (Doctoral dissertation, Christ University, Bangalore).
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 iSEEK 
5 Socol@r  
6 Scribd 
7 WorldCat 
8 SlideShare 
9 PdfSR 
1 R. Brunelli and T. Poggio. “Face recognition: Features versus templates”. IEEE Transaction on Pattern Analysis and Machine Intelligence, 15(10):1042-1062, 1993
2 L. Sirovich and M. Kirby. “Low-Dimensional procedure for the characterization of human face”. Journal of the Optical Society of America A, 4(3):519–524, 1987
3 M. Turk and A. Pentland. “Eigenfaces for Recognition”. Journal of Cognitive Neuroscience, 3(1):71-86, 1991
4 S. Lawrence, C.L. Giles, A. Tsoi and A. Back. “Face recognition: A convolutional neuralnetwork approach”. IEEE Transaction on Neural Networks, 8(1):98-113, 1997
5 W. Zhao, R. Chellappa, P.J. Philips and A. Rosenfeld. “Face recognition: A literature survey”. ACM Computing Surveys 35(4):399-458, 2003
6 X. Tan, S. Chen, Z-H Zhou, and F. Zhang. “Face recognition from a single image per person: a survey”. Pattern Recognition 39:1725-1745, 2006
7 J. Wu and Z.-H Zhou. “Face recognition with one training image per person”. Pattern Recognition Letters, 23(2):1711-1719, 2001
8 H.C. Jung, B.W. Hwang and S.W. Lee. “Authenticating corrupted face image based on noise model”. Proceedings of the 6th IEEE International Conference on Automatic Face and Gesture Recognition, 272, 2004
9 F. Frade, De la Torre, R. Gross, S. Baker, and V. Kumar. “Representational oriented component analysis (ROCA) for face recognition with one sample image per training class”. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition 2:266-273, June 2005.
10 A.M. Martinez. “Recognizing imprecisely localised, partially occluded, and expression variant faces from a single sample per class”. IEEE Transaction on Pattern Analysis and Machine Intelligence 25(6):748-763, 2002
11 B.S. Manjunath, R. Chellappa and C.V.D. Malsburg. “A feature based approach to face recognition”. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition 1:373-378, 1992
12 M. Lades, J.Vorbruggen, J. Buhmann, J. Lange, von der Malsburg and R. Wurtz.“Distortion invariant object recognition in the dynamic link architecture”. IEEE Transaction on Computers 42(3):300-311, 1993
13 X. Tan, S.C. Chen, Z-H Zhou, and F. Zhang. “Recognising partially occluded, expression variant faces from single training image per person with SOM and soft kNN ensemble”.IEEE Transactions on Neural Networks, 16(4):875-886, 2005
14 H.-S. Le and H. Li. “Recognising frontal face images using Hidden Markov models with one training image per person”. Proceedings of the 17th International Conference on Pattern Recognition (ICPR04), 1:318-321, 2004
15 L.R. Rabiner. “A tutorial on Hidden Markov models and selected application in speech recognition”. Proceedings of the IEEE, 77(2):257-286, 1989
16 I. Daubechies, “Orthonormal bases of compactly supported wavelets”. Communication on Pure & Applied Mathematics XLI, 41:909-996, 1988
17 I. Daubechies. “Ten lectures on wavelets”. CBMS-NSF Conference series in Applied Mathematics, No-61, SIAM, Philadelphia Pennsylvania, 1992
18 F. Samaria and A. Harter. “Parameterization of a stochastic model for human face identification”. 2nd IEEE Workshop on Applications of Computer Vision, Saratosa FL.pp.138-142, December, 1994
19 J. Roure, and M. Faundez-Zanuy. “Face recognition with small and large size databases”. Proceedings of 39th Annual International Carnahan Conference on Security Technology, pp 153-156, October 2005.
Mr. OJO, John Adedapo
LAUTECH - Nigeria
Dr. Adeniran, Solomon A.
OAU - Nigeria