Title :
Markov face models
Author :
Dass, Sarat C. ; Jain, Anil K.
Author_Institution :
Dept. of Stat. & Probability, Michigan State Univ., East Lansing, MI, USA
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;
Conference_Titel :
Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7695-1143-0
DOI :
10.1109/ICCV.2001.937692