出版社：University of Malaya * Faculty of Computer Science and Information Technology
摘要：This paper presents an efficient hybrid system to detect frontal faces in colored images regardless of scale, location, illumination, race, number of faces, and complex background. The general architecture of the proposed system encompasses three methods: skin color segmentation, rulebased geometric knowledge, and neural networkbased classifier. In the proposed system, multiskin color clustering models are applied to segment human skin regions, iterative merge stage creates a set of candidate face regions, then, the facial feature segmentation removes false alarms, caused by objects with the color that is similar to skin color. Furthermore, the rulebased geometric knowledge is employed to describe the human face in order to estimate the location of the “face center”. Then the Artificial Neural Network (ANN) face detector is applied only to the regions of the image, which are marked as candidate face regions. The ANN face detector must decide whether a given subwindow of an image contains a face or not. Partial face template is used, instead of the whole face, to reduce face variability, in order to make the training phase easier and to reduce misrecognition. To increase the accuracy of the system, twelvetexture descriptors are calculated and then attached as input data with each face image to train neural network to describe the content of subimage window such as XYRelieves, smoothness, ratio of darkness, etc. Training neural network is designed to be general with minimum customization. Comparisons with other face detection systems have revealed that our system shows better performance in terms of positive and negative detection rates.
关键词：Face Detection; Skin Color Segmentation; Artificial Neural Network; Texture Analysis
