Palm vein recognition is an emerging biometric technology that offers enhanced security and privacy. However, acquiring sufficient palm vein data for training deep learning-based recognition models is challenging due to the high costs of data collection and privacy protection constraints. This has led to a growing interest in generating pseudo-palm vein data using generative models. Existing methods, however, often produce unrealistic palm vein patterns or struggle with controlling identity and style attributes. To address these issues, we propose a novel palm vein generation framework named PVTree. First, the palm vein identity is defined by a complex and authentic 3D palm vascular tree, created using an improved Constrained Constructive Optimization (CCO) algorithm. Second, palm vein patterns of the same identity are generated by projecting the same 3D vascular tree into 2D images from different views and converting them into realistic images using a generative model. As a result, PVTree satisfies the need for both identity consistency and intra-class diversity. Extensive experiments conducted on several publicly available datasets demonstrate that our proposed palm vein generation method surpasses existing methods and achieves a higher TAR@FAR=1e-4 under the 1:1 Open-set protocol. To the best of our knowledge, this is the first time that the performance of a recognition model trained on synthetic palm vein data exceeds that of the recognition model trained on real data, which indicates that palm vein image generation research has a promising future.

翻译：掌脉识别是一种新兴的生物识别技术，具有更高的安全性和隐私性。然而，由于数据采集成本高昂和隐私保护限制，获取足够的掌脉数据用于训练基于深度学习的识别模型具有挑战性。这促使人们越来越关注使用生成模型生成伪掌脉数据。然而，现有方法通常生成的掌脉模式不真实，或在控制身份和风格属性方面存在困难。为解决这些问题，我们提出了一种名为PVTree的新型掌脉生成框架。首先，掌脉身份由一个复杂且真实的3D掌部血管树定义，该血管树使用改进的约束构造优化（CCO）算法创建。其次，通过将同一3D血管树从不同视角投影为2D图像，并使用生成模型将其转换为逼真图像，来生成同一身份的掌脉模式。因此，PVTree同时满足了身份一致性和类内多样性的需求。在多个公开数据集上进行的大量实验表明，我们提出的掌脉生成方法超越了现有方法，并在1:1开集协议下实现了更高的TAR@FAR=1e-4。据我们所知，这是首次在合成掌脉数据上训练的识别模型性能超过在真实数据上训练的识别模型，这表明掌脉图像生成研究具有广阔前景。