Novel and Tuneable Method for Skin Detection Based on Hybrid Color Space and Color Statistical Features
This addresses skin detection for image processing applications like face detection, but it is incremental as it builds on existing color space methods with statistical features.
The paper tackles the problem of skin detection in images, which is inaccurate for all skin types in existing methods, by proposing a hybrid approach using statistical features and HSV/YCbCr color spaces, achieving accuracies of 99.25% on the FEI database and 95.40% on complex backgrounds.
Skin detection is one of the most important and primary stages in some of image processing applications such as face detection and human tracking. So far, many approaches are proposed to done this case. Near all of these methods have tried to find best match intensity distribution with skin pixels based on popular color spaces such as RGB, CMYK or YCbCr. Results show these methods cannot provide an accurate approach for every kinds of skin. In this paper, an approach is proposed to solve this problem using statistical features technique. This approach is including two stages. In the first one, from pure skin statistical features were extracted and at the second stage, the skin pixels are detected using HSV and YCbCr color spaces. In the result part, the proposed approach is applied on FEI database and the accuracy rate reached 99.25 + 0.2. Further proposed method is applied on complex background database and accuracy rate obtained 95.40+0.31%. The proposed approach can be used for all kinds of skin using train stage which is the main advantages of it. Low noise sensitivity and low computational complexity are some of other advantages.