MATLAB Implementation for Hand Gesture Recognition

This article provides an in-depth exploration of MATLAB-based hand gesture recognition. The gesture recognition process involves multiple sequential stages: hand extraction begins with skin color detection using chromatic thresholding techniques, followed by Gaussian filtering for noise reduction through functions like imgaussfilt(). Subsequent sharpening operations enhance edge features using spatial filters, while HU moment calculations (via functions like regionprops()) provide rotation- and scale-invariant shape descriptors for robust pattern matching. These pipeline stages collectively improve accuracy in identifying hand shapes and postures, enabling reliable standalone gesture recognition. Crucially, effective recognition requires meticulous preparation of gesture templates—typically involving normalized sample images stored as reference feature vectors. Developers must also address real-world variables such as varying lighting conditions, hand angles, and partial occlusions by implementing preprocessing adaptations like adaptive thresholding and multi-angle template sets. Proper template design and environmental handling significantly boost recognition rates and system reliability. Key implementation aspects include: - Skin segmentation using YCbCr color space thresholding for illumination invariance - Morphological operations (imopen(), imclose()) to refine hand contours after filtering - HU moment computation via central moment normalization for feature extraction - Template matching algorithms (e.g., Euclidean distance comparison) for gesture classification