Prototype Correction via Contrastive Augmentation for Few-Shot Unconstrained Palmprint Recognition

Unconstrained Palmprint Recognition (UPR) shows engaging potential owing to its high hygiene and privacy. The unconstrained acquisition usually produces wide variations, against which deep methods resort to large samples that are unavailable in practice, however. We focus on Few-Shot UPR (FS-UPR), a more general problem, recognizing query samples given a few support samples per class. Because scarce samples insufficiently represent potential variations, the augmentation methods train independent hallucinators on large samples to generate more ones. Whereas, the hallucinators trained independently of Few-Shot Learning (FSL) are blind of generating promising samples to boost the downstream FSL. Moreover, training hallucinators requires large samples per class, unavailable from unconstrained palmprint databases. We aim to address FS-UPR via contrastive augmentation merely on the support samples. Observing the variations to be transferable across samples, we exploit low-rank representation to disentangle support samples into principles and variations in embedding space and augment features by variation transfer. To this end, we devise an end-to-end Deep Low-Rank Representation Feature Augmentation Network (DLRR-FAN) to simultaneously learn the embedding space and augmentation features with guaranteed reality and diversity. Furthermore, a Contrastive Recognition Regularizer (CRR) is tailored to secure the discriminability of augmentation features. During each training episode, the task motivates DLRR-FAN to augment such features that correct the biased prototypes towards upcoming query samples with variations unseen in the support samples, namely task-driven prototype correction. Extensive experiments on both the typical and extended FS-UPR tasks demonstrate the efficacy of DLRR-FAN versus the state-of-the-art methods.