Summary: FACE DETECTION USING A MIXTURE OF FACTOR ANALYZERS
Ming-Hsuan Yang ? Narendra Ahuja y David Kriegman ?
Department of Computer Science ? Department of Electrical and Computer Engineering y
University of Illinois at Urbana-Champaign, Urbana, IL 61801
E-mail: fmyang1, n-ahuja, kriegmang@uiuc.edu
Web Page: http://vision.ai.uiuc.edu
ABSTRACT
We present a probabilistic method to detect human
faces using a mixture of factor analyzers. One char-
acteristic of this mixture model is that it concurrently
performs clustering and, within each cluster, local di-
mensionality reduction. A wide range of face images
including ones in dierent poses, with dierent expres-
sions and under dierent lighting conditions are used
as the training set to capture the variations of human
faces. In order to t the mixture model to the sam-
ple face images, the parameters are estimated using an
EM algorithm. Experimental results show that faces
in dierent poses, with dierent facial expressions, and
under dierent lighting conditions are accurately de-

Source: Ahuja, Narendra - Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign
Kriegman, David J. - Department of Computer Science and Engineering, University of California at San Diego

Collections: Computer Technologies and Information Sciences; Engineering