Abstract
We propose an approach to cope with the problem of 2D face image recognition system by using 1D Discrete Hidden Markov Model (1D-DHMM). The Haar wavelet transform was applied to the image to lessen the dimension of the observation vectors. The system was tested on the facial database obtained from AT&T Laboratories Cambridge (ORL). Five images of each individuals were used for training, while another five images were used for testing and recognition rate was achieved at 100%, while significantly reduced the computational complexity compared to other 2D-HMM, 2D-PHMM based face recognition systems. The experiments done in Matlab took 1.13 second to train the model for each person, and the recognition time was about 0.3 second.
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Le, HS., Li, H. (2003). Simple 1D Discrete Hidden Markov Models for Face Recognition. In: García, N., Salgado, L., Martínez, J.M. (eds) Visual Content Processing and Representation. VLBV 2003. Lecture Notes in Computer Science, vol 2849. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39798-4_8
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DOI: https://doi.org/10.1007/978-3-540-39798-4_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20081-9
Online ISBN: 978-3-540-39798-4
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