A hybrid approach for static hand gesture recognition: Integrating Directional Adaptive Patterns with Multi-Scale Feature Extraction and Aggregation

This research introduces a hybrid model that combines the strengths of the Directional Adaptive Pattern (DAP) descriptor and the Multi-Scale Feature Extraction and Aggregation Network (MaXNet) to achieve robust and efficient gesture recognition. The primary objective of this study is to enhance accuracy and computational efficiency while ensuring robustness across diverse datasets. The directional adaptive pattern descriptor effectively captures intricate texture details and directional variations by leveraging directional feature analysis, adaptive neighborhood encoding, and multilevel pattern representation. To address the variable-size feature outputs of the proposed descriptor, agglomerative clustering is utilized to generate compact, fixed-size representations, reducing noise while preserving essential texture information. Multi-scale feature extraction and aggregation network further enhances multiscale feature extraction by integrating multi-kernel convolutional layers, depthwise and pointwise convolutions, and hierarchical feature aggregation. Its lightweight and modular design allows for efficient extraction of fine-grained and large-scale patterns while maintaining computational efficiency. The effectiveness of the proposed model is evaluated based on accuracy, precision, recall, and F1-score across ten benchmark datasets. Experimental results show that the proposed model achieves superior accuracy compared to the current state-of-the-art methods.