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Iris recognition based on few-shot learning
Computer Animation and Virtual Worlds ( IF 1.1 ) Pub Date : 2021-05-31 , DOI: 10.1002/cav.2018
Songze Lei 1 , Baihua Dong 1 , Yonggang Li 1 , Feng Xiao 1 , Feng Tian 2
Affiliation  

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.

中文翻译:

基于小样本学习的虹膜识别

虹膜识别是生物识别领域的一个热门研究领域,在自动识别中发挥着重要作用。给定足够的训练数据,一些基于深度学习的方法在虹膜识别上取得了良好的性能。但是,当训练数据有限时,可能会出现过拟合。为了解决这个问题,在本文中,我们提出了一种基于模型不可知元学习(MAML)的虹膜识别小样本学习方法。据我们所知,我们是第一个将小样本学习应用于虹膜识别的人。我们在基准数据集上的实验表明,所提出的方法可以实现比原始 MAML 更高的性能,并且与基于深度学习的方法相比具有竞争力。
更新日期:2021-07-12
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