Rock-Paper-Scissors Gesture Recognition System Using MATLAB with Win/Loss Determination

Implementing a gesture recognition system using MATLAB to play Rock-Paper-Scissors is an engaging project that combines computer vision and pattern recognition techniques. The system requires recognizing user hand gestures (rock, paper, or scissors) from camera input or images, then determining the game outcome according to standard rules. Key implementation involves using MATLAB's Image Processing Toolbox for real-time image acquisition and Computer Vision Toolbox for feature extraction and classification.

Gesture recognition typically follows several critical steps: First, image acquisition captures real-time hand gesture images through a camera interface using functions like videoinput or webcam. Next, image preprocessing techniques including grayscale conversion (rgb2gray), thresholding (imbinarize), and noise removal (medfilt2) enhance gesture features. Feature extraction methods may involve contour detection (bwboundaries), convex hull analysis (regionprops), or deep learning approaches using pre-trained networks like AlexNet or ResNet. Finally, classification matches extracted features against predefined rock, paper, and scissors models using classifiers such as SVM (fitcsvm) or K-NN (fitcknn).

In Rock-Paper-Scissors, each gesture exhibits distinct geometric characteristics. Rock typically appears as a closed fist with rounded contours detectable through circularity measurements. Scissors gesture shows two extended fingers creating noticeable bifurcations in contour analysis. Paper displays a fully open palm with larger contour area calculated using regionprops. These feature differences enable training classifiers to accurately distinguish between gestures through boundary analysis and shape descriptors.

After gesture recognition, the system automatically determines胜负 following the classic rules: rock beats scissors, scissors beat paper, paper beats rock. To improve recognition accuracy, substantial gesture samples should be collected for training datasets, potentially combining multiple image processing techniques like morphological operations (imopen, imclose) and edge detection (edge). This project serves as excellent practice for learning computer vision fundamentals while providing an interactive gaming experience through MATLAB's comprehensive visualization capabilities.