A Contactless Control Mechanism for Computerized Systems using Hand Gestures

This article presents a systematic way to develop a hand-sign recognition system aimed at detecting and interpreting user hand-signs for specific functionalities, enabling an easy contactless user experience. The process unfolds through several sequential steps. Firstly, the data collection phase involves employing a custom camera integrated with OpenCV, capturing a diverse range of hand-signs crucial for model training. Subsequently, the model training step encompasses extracting hand landmark data using mediapipe and leveraging scikit-learn in conjunction with a random forest/SVM classifier. Once the model is trained, a classifier is assigned to recognize distinct hand-signs, achieved by mapping gestures to keyboard shortcuts. Lastly, the system’s functionality is encapsulated within a tkinter GUI, yielding a cohesive user experience. This approach achieves efficient and accurate hand-sign recognition, enabling seamless interaction and activation of functions through gesture-based input in a contactless manner.