Chinese Journal of Computers   Full Text
Title	A survey of human face detection
Authors	LIANG Lu-hong/AI Hai-zhou/XU Guang-you/ZHANG Bo
Address
Year	2002
Issue	No.5(449-458)
Abstract &   Background	This paper presents a survey on the state of the art of face detection research based on systematic analysis of related papers. The literature is reviewed in two parts: feature extraction and feature fusion for face detection. Feature extraction includes skin-color segmentation and various gray level features such as the outline of face, gray level distribution, organic feature, symmetry, template etc. Feature fusion methods include knowledge-based heuristic face verification, statistical learning approaches (Eigenface, Clustering, ANN, SVM, HMM, EM probabilistic model). Performance comparison of some well known methods is given on MIT+CMU test set. In conclusion, statistical learning methods are superior to those knowledge based methods, and in all those learning based methods, the key problem is the training complexity, even by bootstrap method it remains a great challenge due to the diversity of non-face samples compared with face samples. Authors suggest a subspace method for downsizing the training space by designing a filter (such as template matching filter) that excludes most of non face candidates and then training in the downsized subspace. It is pointed out that statistical learning methods depend on the accordance of sample patterns (syntactic information), which cannot take into considerations of much important semantic information. This differs much from human beings in face cognition. There is a limit for statistical only approaches and the help of knowledge based methods is needed. keywords Face detection, Face recognition, Pattern recognition, Computer vision