Face Detection From Color Images Using a Fuzzy Pattern Matching Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Detection in Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical color models with application to skin detection
International Journal of Computer Vision
Skin Color-Based Video Segmentation under Time-Varying Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Skin Segmentation Using Color Pixel Classification: Analysis and Comparison
IEEE Transactions on Pattern Analysis and Machine Intelligence
Differential video coding of face and gesture events in presentation videos
Computer Vision and Image Understanding - Special issue on event detection in video
A survey of skin-color modeling and detection methods
Pattern Recognition
Fast adaptive skin detection in JPEG images
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part II
Face detection using quantized skin color regions merging andwavelet packet analysis
IEEE Transactions on Multimedia
A highly efficient system for automatic face region detection in MPEG video
IEEE Transactions on Circuits and Systems for Video Technology
Face segmentation using skin-color map in videophone applications
IEEE Transactions on Circuits and Systems for Video Technology
Segmentation of the face and hands in sign language video sequences using color and motion cues
IEEE Transactions on Circuits and Systems for Video Technology
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Detection and classification of skin regions plays important roles in many image processing and vision applications. In this paper, we present a statistical approach for fast skin detection in MPEG-compressed videos. Firstly, conditional probabilities of skin and non-skin pixels are extracted from manual marked training images. Then, candidate skin pixels are identified using the Bayesian maximum a posteriori decision rule. An optimal threshold is then obtained by analyzing of probability error on the basis of the likelihood ratio histogram of skin and non-skin pixels. Experiments from sequences with varying illuminations have demonstrated the effectiveness of our approach.