Skin detection by dual maximization of detectors agreement for video monitoring

• We present an approach for adaptive skin detection in images.
• It combines two detectors able to get high and low probable skin pixels.
• Channels of different color spaces are selected to maximize detector performance.
• Results on human activity datasets show improvement over the state-of-the-art.

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.