Palmprint as biometrics has gained increasing attention recently due to its discriminative ability and robustness. However, existing methods mainly improve palmprint verification within one spectrum, which is challenging to verify across different spectrums. Additionally, in distributed server-client-based deployment, palmprint verification systems predominantly necessitate clients to transmit private data for model training on the centralized server, thereby engendering privacy apprehensions. To alleviate the above issues, in this paper, we propose a physics-driven spectrum-consistent federated learning method for palmprint verification, dubbed as PSFed-Palm. PSFed-Palm draws upon the inherent physical properties of distinct wavelength spectrums, wherein images acquired under similar wavelengths display heightened resemblances. Our approach first partitions clients into short- and long-spectrum groups according to the wavelength range of their local spectrum images. Subsequently, we introduce anchor models for short- and long-spectrum, which constrain the optimization directions of local models associated with long- and short-spectrum images. Specifically, a spectrum-consistent loss that enforces the model parameters and feature representation to align with their corresponding anchor models is designed. Finally, we impose constraints on the local models to ensure their consistency with the global model, effectively preventing model drift. This measure guarantees spectrum consistency while protecting data privacy, as there is no need to share local data. Extensive experiments are conducted to validate the efficacy of our proposed PSFed-Palm approach. The proposed PSFed-Palm demonstrates compelling performance despite only a limited number of training data. The codes will be released at https://github.com/Zi-YuanYang/PSFed-Palm.

翻译：掌纹作为一种生物特征，由于其判别能力和鲁棒性，近年来受到越来越多的关注。然而，现有方法主要改善单一光谱下的掌纹验证，难以实现跨光谱验证。此外，在分布式服务器-客户端部署中，掌纹验证系统通常要求客户端将私有数据传输到中央服务器进行模型训练，从而引发隐私担忧。为解决上述问题，本文提出了一种物理驱动的谱一致联邦学习方法用于掌纹验证，称为PSFed-Palm。PSFed-Palm利用不同波长光谱的内在物理特性，即在相似波长下获取的图像呈现更高的相似性。我们的方法首先根据客户端本地光谱图像的波长范围，将其划分为短光谱组和长光谱组。随后，我们引入短光谱和长光谱的锚模型，以约束与长、短光谱图像相关的本地模型的优化方向。具体地，我们设计了一种谱一致损失函数，强制模型参数和特征表示与其对应的锚模型对齐。最后，我们对本地模型施加约束，确保其与全局模型保持一致，有效防止模型漂移。这一措施在保护数据隐私的同时保证了谱一致性，因为无需共享本地数据。大量实验验证了我们提出的PSFed-Palm方法的有效性。尽管仅使用有限数量的训练数据，所提出的PSFed-Palm仍展现出令人信服的性能。代码将发布在https://github.com/Zi-YuanYang/PSFed-Palm。