Computing the Mean Required for Gaussian Model Construction

This document presents a computer algorithm for skin color detection in digital images. Our implementation comprises the following key components: The CENTER module computes the mean values essential for Gaussian model formulation. This typically involves calculating average chromaticity values from training samples in normalized color spaces. CHROMADIST generates scatter plots visualizing skin color distribution in chrominance spaces, aiding in the observation of cluster formations and outlier detection. COLORDISTPLOT creates graphical representations of the Gaussian probability distributions, illustrating the model's covariance characteristics and density contours. The FACE module represents our initial approach using direct RGB thresholding, which was abandoned due to sensitivity to illumination changes and poor generalization across different lighting conditions. ZHIFANGTU (histogram) implements grayscale intensity analysis, providing insights into luminance distribution patterns within skin regions. SKINMODEL constructs the probabilistic skin color model using Gaussian mixture models (GMMs), where the system learns parameters through maximum likelihood estimation on training data. SEGMENTSKIN performs actual skin region segmentation by applying Bayesian classification, followed by morphological operations for region labeling, area-based filtering to remove noise, and aspect ratio validation to eliminate non-facial geometries. Additional utility functions support preprocessing stages (color space conversion, normalization) and postprocessing operations (connected component analysis, boundary refinement). This pipeline enables robust skin region detection with applications spanning image processing, computer vision systems, and biometric authentication technologies. The algorithm demonstrates particular effectiveness in controlled lighting environments with proper parameter tuning.