HUnet++: An Efficient Method for Vein Mask Extraction Based on Hierarchical Feature Fusion

With the development of biometric recognition technology, the technology of vein-based verification has garnered growing interest within the domain of biometric recognition. Nonetheless, the difficulties in differentiating between the background and the vein patterns, as well as the multi-branching, irregularity, and high-precision requirements of the vein structure, often make it difficult to achieve high precision and speed in vein mask extraction. To address this problem, we propose HUnet++, a novel vein recognition method based on the symmetric network structure of the Unet++ model, which enhances the speed of vein mask extraction while maintaining accuracy. The HUnet++ model consists of two main parts: a Feature Capture (FC) module for hierarchical feature extraction, and a Feature Fusion (FF) module for multi-scale feature integration. This structural design bears a striking resemblance to the symmetrical architecture of the Unet++ model, playing a crucial role in ensuring the balance between feature processing and integration. Experimental results show that the proposed method achieves precision rates of 91.4%, 84.1%, 78.07%, and 89.5% on the manually labeled dataset and traditionally labeled datasets (SDUMLA-HMT, FV-USM, Custom dataset), respectively. For a single image with a size of 240 pixels, the feature extraction time is 0.0131 s, which is nearly twice as fast as the original model.