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 have been released at https://github.com/Zi-YuanYang/PSFed-Palm.

掌纹作为生物识别技术由于其区分能力和鲁棒性最近受到越来越多的关注。然而，现有方法主要改进一个光谱内的掌纹验证，这对于跨不同光谱进行验证具有挑战性。此外，在基于服务器-客户端的分布式部署中，掌纹验证系统主要需要客户端传输私有数据以在集中式服务器上进行模型训练，从而产生隐私担忧。为了缓解上述问题，在本文中，我们提出了一种物理驱动的谱一致联邦学习掌纹验证方法，称为 PSFed-Palm。 PSFed-Palm 利用了不同波长光谱的固有物理特性，其中在相似波长下获取的图像显示出高度的相似性。我们的方法首先根据本地光谱图像的波长范围将客户分为短光谱组和长光谱组。随后，我们引入了短光谱和长光谱的锚模型，它限制了与长光谱和短光谱图像相关的局部模型的优化方向。具体来说，设计了一种频谱一致的损失，强制模型参数和特征表示与其相应的锚模型保持一致。最后，我们对局部模型施加约束，以确保其与全局模型的一致性，有效防止模型漂移。该措施保证了频谱一致性，同时保护了数据隐私，因为无需共享本地数据。进行了大量的实验来验证我们提出的 PSFed-Palm 方法的有效性。尽管训练数据数量有限，但拟议的 PSFed-Palm 却表现出了令人信服的性能。代码已发布于 https://github.com/Zi-YuanYang/PSFed-Palm。