Statistical color models with application to skin detection
International Journal of Computer Vision
Pattern Recognition Letters
A survey of skin-color modeling and detection methods
Pattern Recognition
Objective evaluation of approaches of skin detection using ROC analysis
Computer Vision and Image Understanding
Semantic Representation and Recognition of Continued and Recursive Human Activities
International Journal of Computer Vision
Skin detection using pairwise models
Image and Vision Computing
Skin detection for single images using dynamic skin color modeling
Pattern Recognition
Detecting skin in face recognition systems: A colour spaces study
Digital Signal Processing
Shadow detection in video surveillance by maximizing agreement between independent detectors
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Color based skin classification
Pattern Recognition Letters
A semantic-based probabilistic approach for real-time video event recognition
Computer Vision and Image Understanding
PoseShop: Human Image Database Construction and Personalized Content Synthesis
IEEE Transactions on Visualization and Computer Graphics
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This paper presents an approach for skin detection which is able to adapt its parameters to image data captured from video monitoring tasks with a medium field of view. It is composed of two detectors designed to get high and low probable skin pixels (respectively, regions and isolated pixels). Each one is based on thresholding two color channels, which are dynamically selected. Adaptation is based on the agreement maximization framework, whose aim is to find the configuration with the highest similarity between the channel results. Moreover, we improve such framework by learning how detector parameters are related and proposing an agreement function to consider expected skin properties. Finally, both detectors are combined by morphological reconstruction filtering to keep the skin regions whilst removing wrongly detected regions. The proposed approach is evaluated on heterogeneous human activity recognition datasets outperforming the most relevant state-of-the-art approaches.