A Integrated Study Of Face Detection Based On Multi-cue Information In Color Images

Face detection is a hot problem in the fields of computer vision and pattern recognition, with the purpose of localizing and extracting the face region from the background. A human face is a dynamic object and has a high degree of variability in its appearance, which makes face detection a difficult problem in pattern recognition. However, with the further study in face detection, it will surely speed the development of artificial intelligence, computer vision, and other relative subjects. In this paper, a multi-clue based face detection algorithm for color image is presented. First, the image with human faces is input and dealt with a Mixture-of-Gaussian (for multimodal distributions) model and a self-adaptive luminance model to get all skin color regions, as is of great importance to fast face detection although some skin-like color regions are often chosen falsely in the mean time. As a result, a binary image with face regions is obtained. The binary image can be further divided into several sub-regions, which are then analyzed so as to eliminate useless non-face regions according to the facial features and some general knowledge. Consequently, all candidate face regions with special position and size are acquired. And each candidate region is required to include not less than two holes, because double eyes are generally darker than other regions in human face and thus often make holes in their positions. In the end, using Bayes decision, a probability model with the distribution of human face is constructed to perform the final face detection. Experimental results demonstrate the high probability of correct detection and adaptive abilities of the algorithm, and a decent speed of the face detection is achieved.