A color attention mechanism based on YES color space for skin segmentation

Skin segmentation plays an important role in image processing and human–computer interaction tasks. However, it is a challenging task to accurately detect skin regions from various scenes with different illumination or color styles. In addition, in the field of video processing, reducing the computational load and improving the real-time performance of the algorithm has also become an important topic of skin segmentation. Existing deep semantic segmentation networks usually pay too much attention to the detection performance of the model and make the model structure tend to be complex, which brings heavy computational burden. To achieve the trade-off between detection performance and real-time performance of the skin segmentation algorithm, this paper proposes a lightweight skin segmentation network. Compared with existing semantic segmentation networks, this model adopts a simpler structure to improve the real-time performance. In addition, to improve the feature fitting ability of the network without slowing down its inference speed, this paper proposes a color attention mechanism, which locates skin regions in images based on the distribution features of skin colors on the E-R/G color plane generated from the YES color space, and guides the network to update parameters. Experimental results show that this method not only exhibits similar detection performance to existing semantic segmentation networks such as U-Net and DeepLab, but also the computation load of the model is 18.1% lower than Fast-SCNN.