Face Detection

Faces and Gestures

The aforementioned work on representation and learning has contributed to two types of human computer interfaces we have developed. First, learning and classification techniques, including usual statistical classifiers, neural networks, support vector machines and artificial intelligence approaches, have been used to develop new methods for human face detection and hand gesture recognition.

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 that consists of faces in different poses, faces in different expressions and faces under different lighting conditions is 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 facial expressions, and under different lighting conditions are detected by our method.

M.-H. Yang, D. Kriegman and N. Ahuja, Detecting Faces in Images: A Survey, IEEE Transactions on Pattern Analysis and Machine Intelligence, January 2002, 34-58.
M.-H. Yang, D. Kriegman and N. Ahuja, Face Detection Using Multimodal and Density Modes, Computer Vision and Image Understanding, Vol. 84, October 2001, 1-21.
M.-H. Yang and N. Ahuja, Detecting Human Faces in Color Images, IEEE International Conference on Image Processing, Vol. 1, Chicago, IL, October 1998, 127-130.
M.-H. Yang, N. Ahuja, D. Kriegman, Face Detection using a Mixture of Factor Analyzers, International Conference on Image Processing, Kobe, Japan, Oct. 1999, III-612-616.
M.-H. Yang, N. Ahuja and D. Kriegman, Face Detection Using Mixtures of Linear Subspaces, Fourth IEEE Int. Conference on Automatic Face and Gesture Recognition (FG 2000), Grenoble, France, March 2000, 70-76.
D. Roth, M.-H. Yang and N. Ahuja, A SnoW-Based Face Detector, Proc. Advances in Neural Information Processing Systems (NIPSҹ9), Denver, CO, Dec. 1999, 862-868.
M.-H. Yang, D. Roth and N. Ahuja, Face Detection Using Large Margin Classifiers, Proc. International Conference on Image Processing, Thessaloniki, Greece, October 2001, 665-668.