MATLAB Source Code for Skin Color Detection Based on Single Gaussian Model

Skin color detection based on the single Gaussian model represents a significant research direction in computer vision. This algorithm operates by identifying skin-colored pixels within images to enable applications such as face detection, gesture recognition, and dermatological condition analysis. In MATLAB implementation, the process typically involves several preprocessing functions including: - Image grayscale conversion using rgb2gray() function - Color space normalization, often converting RGB to HSV or YCbCr color spaces - Image smoothing through Gaussian filtering with imgaussfilt() to reduce noise Key parameters requiring careful optimization include: - Variance parameters for the Gaussian distribution model - Appropriate threshold values for pixel classification - Color space component ranges that define skin tone characteristics The implementation typically utilizes MATLAB's statistical toolbox functions for Gaussian modeling and may involve probability density function calculations using normpdf(). Achieving optimal detection performance requires meticulous parameter tuning and validation against diverse skin tone datasets. The algorithm's effectiveness depends heavily on proper threshold selection and color space transformation to ensure robust skin pixel classification across varying lighting conditions and ethnicities.