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Iris recognition based on few‐shot learning
Author(s) -
Lei Songze,
Dong Baihua,
Li Yonggang,
Xiao Feng,
Tian Feng
Publication year - 2021
Publication title -
computer animation and virtual worlds
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.225
H-Index - 49
eISSN - 1546-427X
pISSN - 1546-4261
DOI - 10.1002/cav.2018
Subject(s) - computer science , overfitting , artificial intelligence , iris (biosensor) , benchmark (surveying) , deep learning , iris recognition , biometrics , machine learning , field (mathematics) , shot (pellet) , pattern recognition (psychology) , artificial neural network , chemistry , mathematics , geodesy , organic chemistry , pure mathematics , geography
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.