Research Area: Machine Learning
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.
Keywords:
Biometric technology
Few Shot
Iris Recognition
Meta Transfer Learning
AttentionMTL
Author(s) Name: Songze Lei, Baihua Dong, Aokui Shan, Yonggang Li
Journal name: Computers and Electrical Engineering
Conferrence name:
Publisher name: Elsevier
DOI: 10.1016/j.compeleceng.2022.107848
Volume Information: Volume 99
