Robust Palmprint Recognition via Multi-Stage Noisy Label Selection and Correction

Deep learning-based palmprint recognition methods take performance to the next level. However, most current methods rely on samples with clean labels. Noisy labels are difficult to avoid in practical applications and may affect the reliability of models, which poses a big challenge. In this paper, we propose a novel Multi-stage Noisy Label Selection and Correction (MNLSC) framework to address this issue. Three stages are proposed to improve the robustness of palmprint recognition. Clean simple samples are firstly selected based on self-supervised learning. A Fourier-based module is constructed to select clean hard samples. A pototype-based module is further introduced for selecting noisy labels from the remaining samples and correcting them. Finally, the model is trained by using clean and corrected labels to improve the performance. Experiments are conducted on several constrained and unconstrained palmprint databases. The results demonstrate the superiority of our method over other methods in dealing with different noise rates. Compared with the baseline method, the accuracy can be improved by up to 33.45% when there are 60% noisy labels.