Recently, hybrid Convolution-Transformer architectures have become popular due to their ability to capture both local and global image features and the advantage of lower computational cost over pure Transformer models. However, directly embedding a Transformer can result in the loss of convolution-based features, particularly fine-grained features. Therefore, using these architectures as the backbone of a re-identification task is not an effective approach. To address this challenge, we propose a feature fusion gate unit that dynamically adjusts the ratio of local and global features. The feature fusion gate unit fuses the convolution and self-attentive branches of the network with dynamic parameters based on the input information. This unit can be integrated into different layers or multiple residual blocks, which will have varying effects on the accuracy of the model. Using feature fusion gate units, we propose a simple and portable model called the dynamic weighting network or DWNet, which supports two backbones, ResNet and OSNet, called DWNet-R and DWNet-O, respectively. DWNet significantly improves re-identification performance over the original baseline, while maintaining reasonable computational consumption and number of parameters. Finally, our DWNet-R achieves an mAP of 87.53%, 79.18%, 50.03%, on the Market1501, DukeMTMC-reID, and MSMT17 datasets. Our DWNet-O achieves an mAP of 86.83%, 78.68%, 55.66%, on the Market1501, DukeMTMC-reID, and MSMT17 datasets.

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.