Face detection using a mixture of factor analyzers

We present a probabilistic method to detect human faces using a mixture of factor analyzers. One characteristic of this mixture model is that it concurrently performs clustering and, within each cluster, local dimensionality reduction. A wide range of face images including ones in different poses, with different expressions and under different lighting conditions are used as the training set to capture the variations of human faces. In order to fit the mixture model to the sample face images, the parameters are estimated using an EM algorithm. Experimental results show that faces in different poses, with different facial expressions, and under different lighting conditions are accurately detected by our method.