LSFM: Light Style and Feature Matching for Efficient Cross-Domain Palmprint Recognition

The exceptional feature extraction capabilities of deep neural networks (DNNs) have significantly advanced palmprint recognition. However, DNNs typically require training and testing data originate from the same distribution, which limits their practical applications. Moreover, existing unsupervised domain adaptation methods struggle to achieve high accuracy with efficiency. To address these challenges, we propose LSFM, an efficient Light Style and Feature Matching method that enhances palmprint recognition performance in cross-domain scenarios with fewer resources. Specifically, we develop an efficient style transfer model to mitigate domain shifts at the pixel level. We then align features across multiple task-specific layers in high dimensional space to reduce domain discrepancies, further improving cross-domain performance. Finally, we evaluate the effectiveness of the proposed LSFM through extensive experiments on two public multi-domain palmprint databases. The experimental results demonstrate that LSFM achieves superior performance with significantly reduced resource consumption, improving average accuracy to 94.87% and lowering the average equal error rate to 1.46%, while saving over 80% of resources.