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Iris recognition based on few-shot learning.

Lei, S., Dong, B., Li, Y., Xiao, F. and Tian, F., 2021. Iris recognition based on few-shot learning. Computer Animation and Virtual Worlds, 32 (3-4), e2018.

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Iris Recognition Based on Few-Shot Learning.pdf - Accepted Version
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DOI: 10.1002/cav.2018

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.

Item Type:Article
ISSN:1546-4261
Additional Information:Research Funding National Joint Engineering Laboratory of New Network and Detection Foundation. Grant Number: GSYSJ2016008 Shaanxi Science and Technology Plan Project. Grant Number: 2020GY-066
Uncontrolled Keywords:iris recognition; few-shot learning; meta learning; L2 regularization
Group:Faculty of Science & Technology
ID Code:35671
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:22 Jun 2021 09:59
Last Modified:15 Aug 2021 08:29

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