Biometric verification systems are deployed in various security-based
access-control applications that require user-friendly and reliable person
verification. Among the different biometric characteristics, fingervein
biometrics have been extensively studied owing to their reliable verification
performance. Furthermore, fingervein patterns reside inside the skin and are
not visible outside; therefore, they possess inherent resistance to
presentation attacks and degradation due to external factors. In this paper, we
introduce a novel fingervein verification technique using a convolutional
multihead attention network called VeinAtnNet. The proposed VeinAtnNet is
designed to achieve light weight with a smaller number of learnable parameters
while extracting discriminant information from both normal and enhanced
fingervein images. The proposed VeinAtnNet was trained on the newly constructed
fingervein dataset with 300 unique fingervein patterns that were captured in
multiple sessions to obtain 92 samples per unique fingervein. Extensive
experiments were performed on the newly collected dataset FV-300 and the
publicly available FV-USM and FV-PolyU fingervein dataset. The performance of
the proposed method was compared with five state-of-the-art fingervein
verification systems, indicating the efficacy of the proposed VeinAtnNet.

介绍了一种利用卷积多头注意网络的新型指静脉验证技术，该技术能从正常和增强的指静脉图像中提取判别信息，通过与其他五种先进的指静脉验证系统的比较，证明了该方法的有效性。

利用卷积多头注意力网络进行指静脉验证

Fingervein Verification using Convolutional Multi-Head Attention Network

Fine-grained visual categorization (FGVC) aims to discriminate similar
subcategories, whose main challenge is the large intraclass diversities and
subtle inter-class differences. Existing FGVC methods usually select
discriminant regions found by a trained model, which is prone to neglect other
potential discriminant information. On the other hand, the massive interactions
between the sequence of image patches in ViT make the resulting class-token
contain lots of redundant information, which may also impacts FGVC performance.
In this paper, we present a novel approach for FGVC, which can simultaneously
make use of partial yet sufficient discriminative information in environmental
cues and also compress the redundant information in class-token with respect to
the target. Specifically, our model calculates the ratio of high-weight regions
in a batch, adaptively adjusts the masking threshold and achieves moderate
extraction of background information in the input space. Moreover, we also use
the Information Bottleneck~(IB) approach to guide our network to learn a
minimum sufficient representations in the feature space. Experimental results
on three widely-used benchmark datasets verify that our approach can achieve
outperforming performance than other state-of-the-art approaches and baseline
models.

本文提出了一种新颖的 Fine-grained visual categorization (FGVC) 方法，可以同时运用环境线索中局部但足够区分的信息和对目标类别下的冗余信息进行压缩，通过计算批次中高权重区域的比例和自适应阈值，实现了输入空间中背景信息的适度提取。此外，本文还使用信息瓶颈方法指导网络在特征空间中学习最小充分表示。实验结果表明，此方法在三个常用基准数据集上表现优于其他最先进的方法和基线模型。