Iris recognition based on few-shot learning
Authors: Lei, S., Dong, B., Li, Y., Xiao, F. and Tian, F.
Journal: Computer Animation and Virtual Worlds
Volume: 32
Issue: 3-4
eISSN: 1546-427X
ISSN: 1546-4261
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.
https://eprints.bournemouth.ac.uk/35671/
Source: Scopus
Iris recognition based on few-shot learning
Authors: Lei, S., Dong, B., Li, Y., Xiao, F. and Tian, F.
Journal: COMPUTER ANIMATION AND VIRTUAL WORLDS
Volume: 32
Issue: 3-4
eISSN: 1546-427X
ISSN: 1546-4261
DOI: 10.1002/cav.2018
https://eprints.bournemouth.ac.uk/35671/
Source: Web of Science (Lite)
Iris recognition based on few-shot learning.
Authors: Lei, S., Dong, B., Li, Y., Xiao, F. and Tian, F.
Journal: Computer Animation and Virtual Worlds
Volume: 32
Issue: 3-4
ISSN: 1546-4261
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.
https://eprints.bournemouth.ac.uk/35671/
Source: BURO EPrints