High Performance Human Face Recognition using Gabor Based Pseudo Hidden Markov Model

Arindam Kar, Debotosh Bhattacharjee, Mita Nasipuri, Dipak Kumar Basu and Mahantapas Kundu
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Arindam Kar: Indian Statistical Institute, Kolkata, West Bengal, India
Debotosh Bhattacharjee: Department of Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, India
Mita Nasipuri: Department of Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, India
Dipak Kumar Basu: Department of Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, India
Mahantapas Kundu: Department of Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, India

International Journal of Applied Evolutionary Computation (IJAEC), 2013, vol. 4, issue 1, 81-102

Abstract: This paper introduces a novel methodology that combines the multi-resolution feature of the Gabor wavelet transformation (GWT) with the local interactions of the facial structures expressed through the Pseudo Hidden Markov Model (PHMM). Unlike the traditional zigzag scanning method for feature extraction a continuous scanning method from top-left corner to right then top-down and right to left and so on until right-bottom of the image i.e., a spiral scanning technique has been proposed for better feature selection. Unlike traditional HMMs, the proposed PHMM does not perform the state conditional independence of the visible observation sequence assumption. This is achieved via the concept of local structures introduced by the PHMM used to extract facial bands and automatically select the most informative features of a face image. Thus, the long-range dependency problem inherent to traditional HMMs has been drastically reduced. Again with the use of most informative pixels rather than the whole image makes the proposed method reasonably faster for face recognition. This method has been successfully tested on frontal face images from the ORL, FRAV2D, and FERET face databases where the images vary in pose, illumination, expression, and scale. The FERET data set contains 2200 frontal face images of 200 subjects, while the FRAV2D data set consists of 1100 images of 100 subjects and the full ORL database is considered. The results reported in this application are far better than the recent and most referred systems.

Date: 2013
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