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