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A regularized deep self-expression feature augmentation network for few-shot unconstrained palmprint recognition
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DOI：10.1016/j.patcog.2025.112904.png)
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Abstract
En 中文
This paper considers Few-Shot Unconstrained Palmprint Recognition (FS-UPR), a realistic problem in real-world applications, aiming to recognize palmprint images under unconstrained conditions given a few support samples. To date, broad augmentation-based Few-Shot Learning (FSL) methods have emerged to mitigate the sample scarcity. However, large samples are required to train hallucinators, rendering them inapplicable for FS-UPR. This paper addresses FS-UPR via frugal augmentation learning on a few available support samples. Observing that the variations due to various acquisition conditions are transferable across samples, we manage to decompose support samples into principles and variations for variation transfer-based feature augmentation. To this end, we devise a Deep Self-Expression Feature Augmentation Network (DSE-FAN) for simultaneous augmentation learning and FSL. In such an end-to-end manner, downstream tasks drive DSE-FAN to augment features with preserved reality and diversity. The augmented features engage to correct the biased class prototypes to generalize FS-UPR. This process is named task-driven augmentation learning. A tailored Locality Graph regularizer is imposed on DSE to secure augmentation discriminability to further exert the generalization capability. Comprehensive experimental results have verified the efficacy of DSE-FAN against competing methods.
Keywords:
Few-shot unconstrained palmprint recognition
Frugal augmentation learning
Deep self-expression
Variation transfer
Locality graph regularizer
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