DocumentCode :
3299381
Title :
Markov face models
Author :
Dass, Sarat C. ; Jain, Anil K.
Author_Institution :
Dept. of Stat. & Probability, Michigan State Univ., East Lansing, MI, USA
Volume :
2
fYear :
2001
fDate :
2001
Firstpage :
680
Abstract :
The spatial distribution of gray level intensities in an image can be naturally modeled using Markov random field (MRF) models. We develop and investigate the performance of face detection algorithms derived from MRF considerations. For enhanced detection, the MRF models are defined for every permutation of site indices (pixels) in the image. We find the optimal permutation that provides maximum discriminatory power to identify faces from nonfaces. The methodology presented here is a generalization of the face detection algorithm described previously where a most discriminating Markov chain model was used. The MRF models successfully detect faces in a number of test images
Keywords :
Markov processes; computational complexity; face recognition; feature extraction; Markov face models; Markov random field models; face detection algorithms; gray level intensities; optimal permutation; spatial distribution; Computer science; Face detection; Lattices; Markov random fields; Neural networks; Pixel; Probability; Simulated annealing; Statistical distributions; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7695-1143-0
Type :
conf
DOI :
10.1109/ICCV.2001.937692
Filename :
937692
Link To Document :
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