Feature Augmentation Learning for Few-Shot Palmprint Image Recognition With Unconstrained Acquisition

Few-shot learning is challenging in unconstrained palmprint recognition, where the palmprint images are collected by unconstrained acquisitions, i.e., different imaging sensors, backgrounds, palm postures, and illumination conditions. Furthermore, due to the lack of unconstrained palmprint databases and sufficient intra-class samples, it is difficult to apply the classic few-shot techniques, such as pre-training, fine-tuning, and sample augmentation, to generalize the model. In this work, we propose a novel feature augmentation network (FAN) for few-shot unconstrained palmprint recognition. Without any external databases, FAN aims to simultaneously remove the image variations caused by the unconstrained acquisitions and augment their feature representation from only a few support samples. To this end, the proposed deep self-expression module first decouples the support images into their principle and variation features. Assuming that the variations are translational across palm-print samples, the variation-sharing module achieves feature augmentations by swapping and combining all pairs of principle and variation features. The augmented palmprint features generated by FAN enable more general representations of categorical prototypes for few-shot unconstrained palmprint recognition. Experimental results on the standard palmprint databases show that FAN can effectively represent the prototypes of palmprint images from only a few available samples, thus outperforming the state-of-the-art methods in unconstrained palmprint recognition.