Real-Time and Robust Method for Hand Gesture Recognition System Based on Cross-Correlation Coefficient
This addresses gesture recognition for applications like virtual reality and sign language, but it appears incremental as it builds on existing segmentation and feature extraction techniques.
The paper tackles hand gesture recognition by proposing a real-time method using cross-correlation coefficients, achieving an accuracy of 98.34% on the ASL database.
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%.