Contactless palmprints are comprised of both global and local discriminative features. Most prior work focuses on extracting global features or local features alone for palmprint matching, whereas this research introduces a novel framework that combines global and local features for enhanced palmprint matching accuracy. Leveraging recent advancements in deep learning, this study integrates a vision transformer (ViT) and a convolutional neural network (CNN) to extract complementary local and global features. Next, a mobile-based, end-to-end palmprint recognition system is developed, referred to as Palm-ID. On top of the ViT and CNN features, Palm-ID incorporates a palmprint enhancement module and efficient dimensionality reduction (for faster matching). Palm-ID balances the trade-off between accuracy and latency, requiring just 18ms to extract a template of size 516 bytes, which can be efficiently searched against a 10,000 palmprint gallery in 0.33ms on an AMD EPYC 7543 32-Core CPU utilizing 128-threads. Cross-database matching protocols and evaluations on large-scale operational datasets demonstrate the robustness of the proposed method, achieving a TAR of 98.06% at FAR=0.01% on a newly collected, time-separated dataset. To show a practical deployment of the end-to-end system, the entire recognition pipeline is embedded within a mobile device for enhanced user privacy and security.

翻译：非接触掌纹由全局和局部判别特征共同组成。以往研究多侧重于单独提取全局特征或局部特征进行掌纹比对，而本研究提出了一种融合全局与局部特征的新框架，以提高掌纹比对的准确性。借助深度学习的最新进展，本研究整合了视觉Transformer（ViT）和卷积神经网络（CNN），以提取互补的局部与全局特征。并由此开发了基于移动设备的端到端掌纹识别系统——Palm-ID。在ViT和CNN特征的基础上，Palm-ID集成了掌纹增强模块和高效降维模块（用于加速比对）。该系统在精度与延迟之间实现了平衡，提取大小为516字节的模板仅需18毫秒，在对包含10,000个掌纹样本的图库进行检索时，基于AMD EPYC 7543 32核CPU（利用128线程）的搜索时间仅为0.33毫秒。跨数据库比对协议及大规模运行数据集上的评估验证了该方法的鲁棒性，在新收集的时间分离数据集上，当误识率为0.01%时，真正率可达98.06%。为展示端到端系统的实际部署，整个识别流水线被嵌入移动设备中，从而增强了用户隐私与安全性。