Hand Gesture Recognition Using Convolutional Neural Networks

Abstract

Hand gestures play a crucial role in communication and are essential in various scenarios where verbal communication is not possible. For instance, traffic policemen, newsreaders, airport staff, and gamers often rely on hand signals to communicate. Therefore, there is a growing need for robust hand pose recognition (HPR) methods that can identify hand gestures accurately. However, the current state-of-the-art HPR methods struggle with identifying hand gestures in the presence of cluttered backgrounds. To address this challenge, we propose a deep learning framework based on convolutional neural networks (CNNs) to identify hand postures regardless of hand size, location in the image, and background clutter. Our proposed CNN-based approach eliminates the need for feature extraction and learns to recognize hand poses without explicit foreground segmentation. This method effectively identifies hand poses, even in the presence of complex and varying backgrounds or poor lighting conditions. We have conducted several experiments, which demonstrate the superiority of our proposed method over state-of-the-art datasets. Our approach significantly improves the accuracy of hand pose recognition, making it more reliable and efficient for a wide range of applications. Therefore, our proposed method has significant potential for use in real-world scenarios, such as traffic management, sign language interpretation, and virtual reality gaming. Overall, our results suggest that deep neural networks can provide a robust and effective solution for hand gesture recognition tasks.