Control of Computer Pointer Using Hand Gesture Recognition in Motion Pictures
This system provides an alternative input method for computer users, potentially benefiting those who find traditional input devices challenging or prefer gesture-based interaction.
This paper developed a user interface for computer cursor control via hand gestures, using a CNN trained on a custom dataset of 6720 hand images. The system achieved 91.88% accuracy in classifying four hand gestures (fist, palm, pointing left, pointing right) for cursor movement and click actions.
This paper presents a user interface designed to enable computer cursor control through hand detection and gesture classification. A comprehensive hand dataset comprising 6720 image samples was collected, encompassing four distinct classes: fist, palm, pointing to the left, and pointing to the right. The images were captured from 15 individuals in various settings, including simple backgrounds with different perspectives and lighting conditions. A convolutional neural network (CNN) was trained on this dataset to accurately predict labels for each captured image and measure their similarity. The system incorporates defined commands for cursor movement, left-click, and right-click actions. Experimental results indicate that the proposed algorithm achieves a remarkable accuracy of 91.88% and demonstrates its potential applicability across diverse backgrounds.