Control of Computer Pointer Using Hand Gesture Recognition in Motion Pictures

TL;DR

This paper introduces a hand gesture recognition system using CNNs to control computer cursors, achieving high accuracy across diverse backgrounds and enabling intuitive user interface interactions.

Contribution

The study develops a CNN-based gesture recognition system trained on a large, diverse dataset for real-time cursor control and clicking actions.

Findings

Achieved 91.88% gesture classification accuracy.

Demonstrated system effectiveness across various backgrounds.

Enabled intuitive cursor control with hand gestures.

Abstract

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.