Research on Finger-vein Recognition Based on Deep Graph Convolutional Network with Dual-Branch

This study presents a finger-vein recognition method based on a Graph Convolutional Neural Network (GCNN) to overcome the low recognition rates and high computational cost of traditional methods. The study aims to address issues of graph structure instability and degraded matching efficiency in current finger-vein graph models. For this purpose, a Simple Linear Iterative Clustering (SLIC) superpixel segmentation algorithm is utilized to construct a weighted graph, based on which the GCNN is adapted for graph-level feature extraction. A dual-branch multi-interaction deep Graph Convolutional Network (GCN) is proposed to enhance the node's capability to represent higher-order features, to effectively capture these features in the graph data while avoiding oversmoothing. This study first adjusts the graph structure based on node features. Subsequently, by integrating the original and reconstructed graph structures, a dual-branch network architecture is built to fully explore higher-order features. Furthermore, a feature channel interaction mechanism is designed to facilitate information exchange between different branches, thereby improving feature diversity. Experimental results on multiple standard datasets for finger-vein recognition show that the proposed network reduces recognition time per image, improves efficiency, and effectively alleviates oversmoothing. Compared with the single-branch GCN, it improves recognition accuracy by an average of over 1.5 percentage points.