Cross-Domain Palmprint Recognition via Regularized Adversarial Domain Adaptive Hashing

As an effective method of biometrics, palmprint recognition allows the safe identity recognition of humans without spatial and temporal limitations. To build a more robust palmprint recognition system, recent promising Convolutional Neural Networks (CNN) has been incorporated for better palmprint feature extraction and representation. However, the increasing number of palmprint datasets presents us with a cross-domain recognition problem where the upcoming images may come from different imaging conditions compared to the registered palmprints, which will undermine the recognition accuracy significantly. As a supervised approach, the performance of CNN-based model depends on the availability of data and labels from the same domain, which is hard for transferring recognition. To keep the outperforming recognition result of CNN-based models, we propose a novel Regularized Adversarial Domain Adaptative Hashing method (R-ADAH) for cross-domain palmprint recognition based on Deep Hashing Network (DHN). During training, the Maximum Mean Discrepancy (MMD) is incorporated for better adaptive performance. In this scenario, we only train a DHN on the source domain. With the adversarial training, the target network is becoming adaptive to the unlabeled palmprint images with more stable training, unbiased sample gradient and less sensitivity to the hyper-parameter tuning when only domain-specific label is provided. Extensive validation experiments are conducted on benchmark datasets and our self-collected palmprint datasets by mobile phones to test the performance of our model. The results show a promising increase of the recognition performance.