A Two-Stream Convolutional Attention Network for Hand Gesture Recognition and Classification

This study presents a two-stream convolutional attention network for hand gesture recognition and classification, with dual branches that effectively extract a range of spatial features. To further improve its capabilities, Convolutional Neural Network (CNN), Residual Blocks (RB), Attention Mechanism (AM), and Convolutional Block Attention Module (CBAM) are integrated, enabling the network to focus on specific regions of the input while reducing background noise. This structure allows the model to capture variations in hand gestures more effectively. Experiments utilized the Hand-Sign-Images Dataset (HSID) and the Hand Gesture Dataset (HGD), each offering a diverse array of hand gesture patterns. The results indicate that the model achieved high training accuracies of 100% and 98.68%, demonstrating its effectiveness in recognizing complex hand gestures. The proposed approach not only enhances recognition and classification accuracy but also offers strong adaptability, making it suitable for applications such as robot control and assistive communication systems.