MATLAB Implementation of Skin Color Detection and Contour Extraction Algorithm from Literature

Skin color detection and contour extraction represent fundamental tasks in image processing, widely applied in face recognition, gesture analysis, and related domains. The MATLAB implementation typically involves sequential steps including color space conversion, threshold segmentation, and morphological operations. Code implementation generally begins with reading input images using imread() followed by preprocessing with imresize() for standardization.

The core of skin detection lies in selecting appropriate color spaces. Many algorithms adopt YCbCr or HSV color spaces due to their effective separation of luminance and chrominance information. In YCbCr space, skin regions can be accurately segmented by defining threshold ranges for Cb and Cr components using conditional statements like (Cb > min_Cb && Cb < max_Cb && Cr > min_Cr && Cr < max_Cr). The conversion is implemented via rgb2ycbcr() function, while threshold values are often empirically determined through histogram analysis with imhist().

Following binarization, morphological operations (erosion, dilation) are applied to eliminate noise and small non-skin artifacts using imerode() and imdilate() with structuring elements created by strel(). Contour extraction then employs edge detection algorithms (Canny via edge(I,'canny') or Sobel) or connected component analysis through bwconncomp(). Post-extraction, contour simplification may utilize convex hull algorithms (convhull()) or polygon approximation methods.

Algorithm performance critically depends on threshold selection and morphological parameter tuning. Practical implementations often require dynamic adaptation based on lighting conditions and ethnic skin variations through automated threshold calibration using graythresh() or adaptive techniques. This combined approach leveraging color and geometric features achieves an optimal balance between computational efficiency and accuracy, with potential optimization through GPU acceleration using gpuArray() for large-scale processing.