Gabor‐Augmented Two‐Stream Framework With GoogLeNet ‐Based Temporal Attention for Semantic Dynamic Hand Gesture Recognition

Hand gesture recognition portrays a vital role in prompting a realistic interaction between humans and computers. It has potential around diverse domains, such as assistive technologies, virtual reality and healthcare. Even though conventional static gesture recognition has attained significant advancements, dynamic gestures are more challenging to recognize because they rely on occlusions, spatiotemporal cues and environmental changes. While current deep learning and transformer‐based models enhanced accuracy, there remain some issues related to interpretability, computational efficiency and robustness. This research presents a Gabor‐augmented with Temporal Attention and User‐interface Framework (GTAU‐F), implemented for recognizing semantic dynamic hand gestures. GTAU‐F emphasizes a Gabor‐enhanced inflated 3D two‐stream network for extracting spatio‐temporal features, together with an attention‐guided GoogLeNet that integrates temporal encoding and builder optimization for coherent convergence. To facilitate certain interpretation, Gradient‐weighted Class Activation Mapping++ (Grad‐CAM++) visualizations are employed, and a human‐centric adaptive gesture interface facilitates real‐time, user‐friendly interactions. Assessment outcomes based on four datasets (ChaLearn LAP IsoGD, NvGesture, EgoGesture, and Hand gesture recognition database) illustrate that GTAU‐F attains over 99% in accuracy, precision, recall, and specificity. Finally, GTAU‐F emerges as an efficient, robust and interpretable solution for enhancing dynamic gesture recognition in human–computer interactions.