  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.
