3D Facial Feature Extraction and Global Motion Recovery Using Multi-modal Information
BMVC '00 Proceedings of the First IEEE International Workshop on Biologically Motivated Computer Vision
Understanding transit scenes: a survey on human behavior-recognition algorithms
IEEE Transactions on Intelligent Transportation Systems
| Hi-index | 0.00 |
This paper proposes an object oriented face detection method using multi-modal fusion of range, color and motion information. Objects are segmented from complex background using stereo disparity histogram that represents the range information of the objects. Matching pixel count (MPC) disparity measure is introduced to enhance the matching accuracy. To detect the facial regions among segmented objects, skin-color transform technique is used with the general skin color distribution (GSCD) modeled by 2D Gaussian function in a Color Synthetic Normalization(CSN) color space.Motion detection technique of AWUPC(Adaptive Weighted Unmatched Pixel Count) is defined on the skin-color transformed image where 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 moving human object in various environments such as skin color noise and complex background. It can be useful in MPEG-4 SNHC