Face detection using multi-modal information

This paper proposes an object-oriented face detection method using multi-modal fusion of range, color and motion information. Objects are segmented from a complex background using a stereo disparity histogram that represents the range information of the objects. A matching pixel count (MPC) disparity measure is introduced to enhance the matching accuracy. To detect the facial regions among segmented objects, a skin-color transform technique is used with the general skin color distribution (GSCD) modeled by a 2D Gaussian function in a color synthetic normalization (CSN) color space. The motion detection technique of AWUPC (adaptive weighted unmatched pixel count) is defined on the skin-color transformed image where the adaptive threshold value for the motion detection is determined according to the probability of skin color. AWUPC transforms the input color image into a gray-level image that represents the probability of both the skin color and motion information. The experimental results show that the proposed algorithm can detect a moving human object in various environments such as skin color noise and complex background. It can be useful in MPEG-4 SNHC