Attention meta-transfer learning approach for few-shot iris recognition
COMPUTERS & ELECTRICAL ENGINEERING(2022)
Abstract
Iris recognition is a very important biometric technology. Given sufficient labeled data, iris recognition algorithms combined with deep learning have achieved excellent performance. With limited training samples, however, over-fitting often occurs and affects recognition performance if deep learning methods are directly used for training. The learning problem with insufficient samples may be solved by using few-shot learning methods. In this paper, we propose an attention meta-transfer learning (AttentionMTL) approach for iris recognition through an improved attention network model. Experiments on the publicly available datasets show that AttentionMTL has achieved the highest accuracy of 99.95% and obtained higher accuracy (up to 6%) than conventional MTL method and other related approaches.
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Key words
Meta-transfer learning,Few-shot learning,Attention,Iris recognition
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