As a representative of a new generation of biometrics, vein identification technology offers a high level of security and convenience. Convolutional neural networks (CNNs), a prominent class of deep learning architectures, have been extensively utilized for vein identification. Since their performance and robustness are limited by small Effective Receptive Fields (e.g. 3$\times$3 kernels) and insufficient training samples, however, they are unable to extract global feature representations from vein images in an effective manner. To address these issues, we propose StarLKNet, a large kernel convolution-based palm-vein identification network, with the Mixup approach. Our StarMix learns effectively the distribution of vein features to expand samples. To enable CNNs to capture comprehensive feature representations from palm-vein images, we explored the effect of convolutional kernel size on the performance of palm-vein identification networks and designed LaKNet, a network leveraging large kernel convolution and gating mechanism. In light of the current state of knowledge, this represents an inaugural instance of the deployment of a CNN with large kernels in the domain of vein identification. Extensive experiments were conducted to validate the performance of StarLKNet on two public palm-vein datasets. The results demonstrated that StarMix provided superior augmentation, and LakNet exhibited more stable performance gains compared to mainstream approaches, resulting in the highest recognition accuracy and lowest identification error.

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