Physics-Driven Spectrum-Consistent Federated Learning for Palmprint
  Verification
Ziyuan Yang Andrew Beng Jin Teoh Bob Zhang Lu Leng Yi Zhang
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.