Beyond Static Features: A Novel Dynamic Palmprint Verification Framework Empowered by Generative Models

Palmprint recognition has received considerable attention due to its inherent discriminative characteristics. However, conventional methods largely rely on static features extracted from individual images, which limits their representational richness. To address this, we propose a dynamic palmprint verification framework that harnesses generative models to enhance feature representations through dynamic construction and matching strategies. During training, a classifier-guided generative model synthesizes class-aware pairs, and a regularization term is introduced to expand the feature space, while mitigating overfitting. For matching, we reformulate the process as a subspace projection within a locally adaptive feature space, where the original and class-conditioned generated features form the basis of the subspace. This enables the model to capture latent inter-individual relationships and achieve stronger discriminability. Extensive experiments across multiple backbones and public benchmarks validate the effectiveness and robustness of the proposed framework.