Design of an Improved Model for Vein Detection Using Attention-Augmented Graph Convolutional Networks and Layer-Wise Adaptive Attention Network

Abstract

The need to detect veins in hand images becomes critical in applications dealing with biometrics, medical diagnostics, and security systems. Though the models developed so far—especially the ones based on CNN architectures—are well received, they lack in capturing the intricate vein patterns because of the inherent focus of CNNs on local features, and hence yield suboptimal performance. Therefore, this paper proposes state-of-the-art models that incorporate attention mechanisms and graph neural networks to enhance feature extraction and model efficiency. First, we propose the Attention-Augmented Graph Convolutional Network (AAGCN). AAGCN combines CNNs with GNNs, which are further modified by incorporating attention mechanisms. The CNN layers extract local features from hand images, which are then translated into a graph representation. Each node in the graph corresponds to key feature points, and edges correspond to their spatial relationship with one another. The GNN layers then diffuse the information within this graph to capture the global vein structure. The attention mechanism dynamically focuses on the most relevant nodes and edges, yielding a 15% improvement in detection accuracy and a 20% reduction in inference time compared with the baseline CNN models. Next, we propose the Layer-Wise Adaptive Attention Network, applying attention mechanisms at multiple layers within the CNN. That allows it to adaptively focus on the important features at different levels of abstraction. The progressive training and knowledge distillation reduce the training time for LWAAN by 30% and the model size by 25%, making it viable for portable devices without any loss of accuracy. Last but not least, we propose the Graph-Attention EfficientNet, which fuses EfficientNet with GNN and attention mechanisms. The key backbone is formed by using EfficientNet for efficient feature extraction and scaling. Then, the construction of the graph and the attention layers from the GNN project importance on the significant vein structures. Finally, further efficiency is achieved with the help of optimization techniques like model pruning and quantization, further making this model achieve 10% better accuracy and 25% faster inference compared to the standard EfficientNet models. Impacts of this work are tremendous: a comprehensive improvement in the accuracy and efficiency of vein detection models. These developments create new paths for real-time applications in diverse fields to ensure robust performance and scalability. Integrating the attention mechanism within the architecture of GNN and CNN proves to be a breakthrough innovation that resets the benchmark for future research in the area of vein detection and all the related biometric applications.