计算机应用与软件2024，Vol.41Issue(6)：186-193,8.DOI:10.3969/j.issn.1000-386x.2024.06.028
基于深度学习的眼周识别方法研究
PERIOCULAR RECOGNITION APPROACH BASED ON DEEP LEARNING
摘要
Abstract
In order to improve the performance of periocular recognition,a new method based on deep convolutional neural networks referred to as PeriocularNet is proposed.PeriocularNet exploited a 16-layer convolutional neural network,integrated with a residual learning module,and adopted the ArcFace loss function.Data augmentation was introduced to avoid the over-fitting in training process.The experiments on UBIPr and UBIRIS.V2 datasets show that the equal error rate(EER)of the proposed approach achieve 1.9％and 7.9％respectively.which improves the periocular recognition performance compared to the related methods.In addition,in order to verify the effect of the eyebrow region feature on the performance of periocular recognition in the end-to-end approach,two periocular datasets,UBIPr-1 and UBIRIS-1,involving three eyebrow shapes were established.Experimental results show that the EER of images containing the eyebrow feature is lower than that of the eyebrow feature removed,which indicates the importance of eyebrow feature in periocular recognition.关键词
生物特征识别/眼周识别/深度学习/卷积神经网络/眉毛区域Key words
Biometric recognition/Periocular recognition/Deep learning/Convolutional neural network/Eyebrow area分类
信息技术与安全科学引用本文复制引用
秦涛,王云龙,孙哲南,周琬婷..基于深度学习的眼周识别方法研究[J].计算机应用与软件,2024,41(6):186-193,8.基金项目
科技部国家重点研发计划项目(2017YFC0821602) （2017YFC0821602）
国家自然科学基金青年科学基金项目(62006225,62006227). （62006225,62006227）
