MMRAN: A novel model for finger vein recognition based on a residual attention mechanism MMRAN: A novel finger vein recognition model

Finger vein recognition is an advanced biometric recognition technology that offers high precision and high security. It recognizes or authenticates individuals using irradiating vein texture images collected from fingers with near-infrared light. In this paper, we propose a new finger vein recognition model (MMRAN) based on a multiscale and multistage residual attention network. First, to fully adapt to the low-resolution, grayscale pixels, and linear patterns of finger vein images, we designed an architecture that combines a fusion residual attention block (FRAB) and a multistage residual attention connection (MRAC). The FRAB contains two distinct subpaths: the main vein path (MVP) and the guided attention path (GAP). The MVP extracts finger vein features at multiple scales by using a multibranch residual structure, while the GAP uses an hourglass network to generate weight maps to guide the setting of eigenvalues at corresponding locations in the main vein feature map. MRAC integrates venous features extracted at different learning stages through the above two pathways. The proposed multiscale and multistage extraction model is effective at extracting various types of digital vein features, including those whose shapes change in width, direction, curvature, and so on. We combine the various dimensions of vein features through multistage learning to further improve the model performance for extracting high-level abstract features. To evaluate the performance of our proposed model, we conducted a large number of experiments on five publicly available finger vein datasets. The experimental results show that the proposed model not only achieves a recognition accuracy above 98%, which is an improvement compared to the current state-of-the-art methods, but it can also be implemented with fewer parameters, which improves training and inference.