Skin Gaussian Model with Trained Threshold for Detection

The program mentioned in this context is a custom-developed application that provides results through a graphical user interface (GUI). This implementation is based on a self-constructed Gaussian model where thresholds are obtained during the training process. The program achieves skin detection by segmenting skin regions in the YCbCr color domain, utilizing color space transformation and probability modeling for accurate identification.

To provide more detailed explanations, I can add the following technical aspects: The program performs skin detection through color analysis and modeling of skin tones. It employs a Gaussian Mixture Model (GMM) for color distribution modeling, where the implementation involves calculating probability densities for skin and non-skin pixels. The thresholding mechanism then distinguishes skin regions from non-skin areas based on these probability distributions. Through model training, appropriate thresholds are determined to accurately identify skin regions, with the training process typically involving maximum likelihood estimation or EM algorithm optimization. Performing skin segmentation in the YCbCr color space enhances detection accuracy by leveraging the separation of luminance (Y) and chrominance (Cb, Cr) components, which makes the model less sensitive to lighting variations. Furthermore, the program includes adaptive parameters that can be adjusted to accommodate different skin types and various lighting conditions, with the code containing configurable threshold values and color space conversion functions for flexibility.