Improvement of Non-frontal Face Skin Color Model Based on YCgCr Color Space

In non-frontal situations, the image generally does not contain complete facial information, and skin color is the most important feature for detecting human faces. In order to improve the detection effect of non-frontal face skin color and applicable scenarios, this paper first analyzes and compares the YCbCr and YCgCr color spaces. Then, by comparing the fixed threshold method, the Gaussian skin color model method and the Otsu method, it is found that the Gaussian skin color model method is the most effective method, but still needs improvement. This paper proposes the Otsu method to optimize the threshold calculation in the Gaussian skin model, and the nonlinear segmented ellipse model reduces the impact of brightness. Experimental results show that this method can eliminate skin-like backgrounds more effectively, and can still maintain a better detection rate under dim lighting, and has a more ideal detection effect.