Recognition of Five Basic Static Hand Gestures

This code implements recognition of five basic static hand gestures. The algorithm first segments the hand region using a skin color model that processes images in YCbCr color space for robust skin detection under varying lighting conditions. Next, it employs boundary tracing techniques (using methods like Moore-Neighbor tracing) to extract precise hand contours from the segmented regions. Finally, gesture identification is achieved through Fourier descriptors calculated from contour coordinates, where the lower-frequency Fourier coefficients capture essential shape characteristics while filtering out noise. The implementation includes Hu moment invariants for supplementary shape analysis, ensuring reliable classification of gestures like open palm, fist, victory sign, thumb-up, and pointing hand. This efficient implementation supports various applications including gesture-controlled gaming systems and security systems with gesture-based authentication. Users can readily integrate this solution for gesture recognition applications, with modular functions provided for each processing stage. For technical inquiries or implementation support, please feel free to consult.