Learning to Generalize Unseen Dataset for Cross-Dataset Palmprint Recognition

Cross-dataset palmprint recognition promotes the convenience and flexibility of palmprint recognition. However, most of the current cross-dataset palmprint recognition methods need to collect the target dataset in advance for model training. They tend to overfit this target dataset and are difficult to generalize to other unknown datasets. In this paper, we propose a novel Palmprint Data and Feature Generation (PDFG) method for a more challenging scenario, Cross-Dataset Palmprint Recognition with Unseen Target dataset (CDPR-UT). Both data-level and feature-level generalization is constructed to improve the adaptability of model to unknown target datasets. A Fourier-based data augmentation method is firstly introduced to generate more training data with new styles. Then several effective losses are constructed at feature level to reduce the shifts between source and augmented datasets and extract adaptive features. Experiments are conducted on multiple palmprint datasets. The results demonstrate that our method is more efficient and robust in dealing with CDPR-UT than other methods. Compared with the baseline, the accuracy is improved by up to 18.20% and the Equal Error Rate (EER) is reduced by up to 12.53%.