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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
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KEYWORDS
Hidden Markov Model (HMM), Recognition Rate (RR), False Acceptance Rate (FAR), Face Recognition (FR)
ABSTRACT
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).
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Mr. OJO, John Adedapo
LAUTECH - Nigeria
jaojo@lautech.edu.ng
Dr. Adeniran, Solomon A.
OAU - Nigeria