Iris recognition based on few-shot learning.

Abstract

Iris recognition is a popular research field in the biometrics, and it plays an important role in automatic recognition. Given sufficient training data, some deep learning-based approaches have achieved good performance on iris recognition. However, when the training data are limited, overfitting may occur. To address this issue, in this paper, we proposed a few-shot learning approach for iris recognition, based on model-agnostic meta-learning (MAML). To our best knowledge, we are the first to apply few-shot learning for iris recognition. Our experiments on the benchmark datasets have demonstrated that the proposed approach can achieve higher performance than the original MAML, and it is competitive to deep learning-based approaches.