Featured Project
Gesture Controlled Rock Paper Scissors
This game uses MediaPipe landmark detection to gather information about the player's hand's position. These landmarks are fed into a custom model trained using Scikit Learn while the computer generates random actions to compete with.
What it does
This project turns hand gestures into playable rock-paper-scissors input, letting the user compete against a computer using realtime vision-based classification. It combines camera input, landmark extraction, model inference, and game response in one loop.
How it works
MediaPipe is used to detect and track hand landmarks from the live camera feed, and those features are passed into a trained Scikit Learn classifier to identify the gesture. The predicted move is then fed directly into the game logic so the match updates in realtime.