Face Recognition Based on Fractal and Hidden Markov Model

2010 ◽  
Author(s):  
ZhiPeng Sun ◽  
JinGuang Sun
Keyword(s):  
Face Recognition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov

Face recognition in constrained environment using hidden Markov model approach

2009 ◽  
Author(s):  
P. Shah ◽  
S. Kar ◽  
S. Hiremath ◽  
D.G. Joshi ◽  
V.K. Chadda
Keyword(s):  
Face Recognition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Model Approach

scholarly journals Face Recognition Using Wavelet Coefficients and Hidden Markov Model

2003 ◽  
Vol 13 (6) ◽  
pp. 673-678 ◽  
Author(s):  
Kyung-Ah Lee ◽  
Dae-Jong Lee ◽  
Jang-Hwan Park ◽  
Myung-Geun Chun
Keyword(s):  
Face Recognition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Wavelet Coefficients

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

2013 ◽  
Vol 4 (1) ◽  
pp. 81-102 ◽  
Author(s):  
Arindam Kar ◽  
Debotosh Bhattacharjee ◽  
Mita Nasipuri ◽  
Dipak Kumar Basu ◽  
Mahantapas Kundu
Keyword(s):  
Face Recognition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
High Performance ◽  
Hidden Markov ◽  
Gabor Wavelet ◽  
Data Set ◽  
Scanning Method ◽  
Face Images ◽  
Gabor Wavelet Transformation

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.

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