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.

翻译：作为新一代生物识别技术的代表，静脉识别技术具有高安全性和便利性。卷积神经网络作为深度学习架构的重要分支，已广泛用于静脉识别。然而，由于受限于较小的有效感受野（如3×3卷积核）和训练样本不足，现有方法难以从静脉图像中有效提取全局特征表征。针对这些问题，我们提出StarLKNet——基于大核卷积与混合增强方法的掌静脉识别网络。通过StarMix方法有效学习静脉特征分布以扩充样本，同时探索卷积核尺寸对识别网络性能的影响，设计了融合大核卷积与门控机制的LaKNet网络，使其能从掌静脉图像中捕获全局特征表征。据现有文献检索，这是大核卷积神经网络在静脉识别领域的首次应用。在两个公开掌静脉数据集上的大量实验验证了StarLKNet的性能，结果表明StarMix具有卓越的数据增强能力，LaKNet相较主流方法展现出更稳定的性能提升，最终实现了最高的识别准确率和最低的识别误差。