Gesture Based Game Control Using Machine Learning

Convolutional Neural Networks (CNNs) for image analysis and other computer vision techniques will be used to detect hand and facial motions. This will enable the system to accurately interpret user gestures and map them to specific in-game actions. Human computer interaction includes hand gesture recognition as a means of presenting the user interface. Various machine learning algorithms, including but not limited to, deep learning models (CNNs and Recurrent Neural Networks), decision trees, and ensemble methods, will be explored to classify and interpret the extracted features, translating them into meaningful game commands. Low latency processing will be a priority to ensure that the system responds quickly to user gestures, maintaining a seamless gaming experience.