Real-Time and Robust Method for Hand Gesture Recognition System Based on Cross-Correlation Coefficient

TL;DR

This paper introduces a real-time hand gesture recognition system using cross-correlation coefficient for feature extraction, achieving high accuracy and robustness suitable for applications like sign language and virtual reality.

Contribution

The paper presents a novel real-time gesture recognition method combining image segmentation and cross-correlation, demonstrating improved accuracy on ASL data.

Findings

Achieved 98.34% accuracy on ASL database

Effective in real-time gesture recognition scenarios

Robust to variations in gesture presentation

Abstract

Hand gesture recognition possesses extensive applications in virtual reality, sign language recognition, and computer games. The direct interface of hand gestures provides us a new way for communicating with the virtual environment. In this paper a novel and real-time approach for hand gesture recognition system is presented. In the suggested method, first, the hand gesture is extracted from the main image by the image segmentation and morphological operation and then is sent to feature extraction stage. In feature extraction stage the Cross-correlation coefficient is applied on the gesture to recognize it. In the result part, the proposed approach is applied on American Sign Language (ASL) database and the accuracy rate obtained 98.34%.