计算机科学

计算机科学 ›› 2025, Vol. 52 ›› Issue (4): 185-193.doi: 10.11896/jsjkx.250100022

• 计算机图形学&多媒体 • 上一篇    下一篇

基于虹膜纹理感知的自适应关联学习方法

孔佳琳1, 张琪1, 卫建泽2, 李琦3   

  1. 1 中国人民公安大学信息网络安全学院 北京 100038
    2 中国科学院微电子研究所 北京 100029
    3 中国科学院自动化研究所 北京 100190
  • 收稿日期:2025-01-03 修回日期:2025-02-17 出版日期:2025-04-15 发布日期:2025-04-14
  • 通讯作者: 张琪(qi.zhang@ppsuc.edu.cn)
  • 作者简介:(lynn00660@163.com)
  • 基金资助:
    国家自然科学基金(61906199,62306307);中国博士后科学基金(2024T170985)

Adaptive Contextual Learning Method Based on Iris Texture Perception

KONG Jialin1, ZHANG Qi1, WEI Jianze2, LI Qi3   

  1. 1 School of Information and Cyber Security,People’s Public Security University of China,Beijing 100038,China
    2 Institute of Microelectronics,Chinese Academy of Sciences,Beijing 100029,China
    3 Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China
  • Received:2025-01-03 Revised:2025-02-17 Online:2025-04-15 Published:2025-04-14
  • About author:KONG Jialin,born in 2000,master candidate,is a member of CCF(No.R3399G).Her main research interests include artificial intelligence and biometrics,etc.
    ZHANG Qi,born in 1988,Ph.D,asso-ciate professor.Her main research in-terests include biometrics,machine learning,etc.
  • Supported by:
    National Natural Science Foundation of China(61906199,62306307) and China Postdoctoral Science Foundation(2024T170985).

PDF (PC)

96

摘要： 虹膜中的微结构具有较高的个体区分度,使得虹膜识别成为实现身份验证的理想选择。除了微结构本身的特征外,其间的关联性也是用于身份验证的有效判别线索。针对虹膜微结构之间的关联性,提出了一种基于虹膜纹理感知的自适应关联学习方法,该方法在关联测度模型的双分支结构基础上进行改进,融入了通道注意力和高效多尺度注意力机制来自适应地动态调整特征图,从不同细节层次的分布中捕捉特征,提高了对虹膜微结构的敏感度。为了深入挖掘来自全局和局部特征之间的关联作用,利用注意力机制对双分支网络提取到的特征进行自适应加权融合,这种加权方式可以根据输入的重要性或相关性灵活地分配不同的权重以学习最优特征关联。实验结果表明,自适应关联学习方法在虹膜识别任务中的表现出色,在多项评价指标上优于现有基线方法,具有更高的识别精度和更强的泛化能力。

关键词: 虹膜识别, 生物特征识别, 深度学习, 注意力机制, 自适应

Abstract: The microstructures in the iris exhibit a high degree of individual distinctiveness,making iris recognition an ideal choice for identity verification.In addition to the characteristics of the microstructures themselves,the context among them also serves as an effective cue for identity verification.To address the correlations between iris microstructures,an adaptive contextual learning method based on iris texture perception is proposed.This method improves upon the dual-ranch structure of contextual measures model by incorporating channel attention and efficient multi-scale attention mechanisms.These mechanisms dynamically adjust the feature maps adaptively,capturing features from different levels of detail distribution and enhancing sensitivity to iris microstructures.To thoroughly explore the correlation between global and local features,attention mechanisms are employed to adaptively fuse the features extracted by the dual-branch network.This weighting approach flexibly assigns diffe-rent weights based on the importance or relevance of the input,aiming to learn the optimal feature associations.The experimental results demonstrate that the adaptive contextual learning method performs excellently in iris recognition tasks,surpassing existing baseline methods across multiple evaluation metrics,with higher recognition accuracy and stronger generalization ability.

Key words: Iris recognition, Biometric recognition, Deep learning, Attention mechanism, Adaptive

中图分类号: 

  • TP391.41
[1]HE Z,TAN T,SUN Z,et al.Toward accurate and fast iris segmentation for iris biometrics[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2008,31(9):1670-1684.
[2]DAUGMAN J G.High confidence visual recognition of persons by a test of statistical independence[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,15(11):1148-1161.
[3]DAUGMAN J.How iris recognition works[M]//The essential guide to image processing.Academic Press,2009:715-739.
[4]MA L,TAN T,WANG Y,et al.Efficient iris recognition by characterizing key local variations[J].IEEE Transactions on Image Processing,2004,13(6):739-750.
[5]SUN Z,TAN T.Ordinal measures for iris recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2008,31(12):2211-2226.
[6]PROENÇA H,NEVES J C.IRINA:Iris recognition(even) in inaccurately segmented data[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:538-547.
[7]LIU N,LIU J,SUN Z,et al.A code-level approach to heterogeneous iris recognition[J].IEEE Transactions on Information Forensics and Security,2017,12(10):2373-2386.
[8]NALLA P R,KUMAR A.Toward more accurate iris recognition using cross-spectral matching[J].IEEE Transactions on Image Processing,2016,26(1):208-221.
[9]LECUN Y,BENGIO Y,HINTON G.Deep learning[J].Nature,2015,521(7553):436-444.
[10]SCHMIDHUBER J.Deep learning in neural networks:An overview[J].Neural Networks,2015,61:85-117.
[11]ZHAO Z,KUMAR A.Towards more accurate iris recognition using deeply learned spatially corresponding features[C]//Proceedings of the IEEE International Conference on Computer Vision.2017:3809-3818.
[12]ZHANG Q,LI H,SUN Z,et al.Deep feature fusion for iris and periocular biometrics on mobile devices[J].IEEE Transactions on Information Forensics and Security,2018,13(11):2897-2912.
[13]KRIZHEVSKY A,SUTSKEVER I,HINTON G E.Imagenetclassification with deep convolutional neural networks[J].Advances in Neural Information Processing Systems,2012,25:1097-1105.
[14]SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[J].arXiv:1409.1556,2014.
[15]REN M,WANG Y,SUN Z,et al.Dynamic graph representation for occlusion handling in biometrics[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2020:11940-11947.
[16]WEI J,WANG Y,HUANG H,et al.Contextual measures for iris recognition[J].IEEE Transactions on Information Forensics and Security,2022,18:57-70.
[17]OUYANG D,HE S,ZHANG G,et al.Efficient multi-scale attention module with cross-spatial learning[C]//2023 IEEE International Conference on Acoustics,Speech and Signal Proces-sing(ICASSP 2023).IEEE,2023:1-5.
[18]CHEN H,HU G,LEI Z,et al.Attention-based two-stream convolutional networks for face spoofing detection[J].IEEE Tran-sactions on Information Forensics and Security,2019,15:578-593.
[19]GANGWAR A,JOSHI A,JOSHI P,et al.Deepirisnet2:Learning deep-iriscodes from scratch for segmentation-robust visible wavelength and near infrared iris recognition[J].arXiv:1902.05390,2019.
[20]JIA B,FENG X X,LI J,et al.Pilot Iris Recognition Based on Spherical Haar Wavelet and Convolutional Neural Network[J].Journal of Electronics & Information Technology,2021,43(4):939-947.
[21]YAN Z,HE L,WANG Y,et al.Flexible iris matching based on spatial feature reconstruction[J].IEEE Transactions on Biometrics,Behavior,and Identity Science,(Early Access).
[22]PECORARO R,BASILE V,BONO V.Local multi-head channel self-attention for facial expression recognition[J].Information,2022,13(9):419.
[23]HU J,SHEN L,SUN G.Squeeze-and-excitation networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:7132-7141.
[24]WANG Q,WU B,ZHU P,et al.ECA-Net:Efficient channel attention for deep convolutional neural networks[C]//Procee-dings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:11534-11542.
[25]FANG M,DAMER N,BOUTROS F,et al.Iris presentation attack detection by attention-based and deep pixel-wise binary supervision network[C]//2021 IEEE International Joint Confe-rence Bn biometrics(IJCB).IEEE,2021:1-8.
[26]WOO S,PARK J,LEE J Y,et al.Cbam:Convolutional block attention module[C]//Proceedings of the European Conference on Computer Vision(ECCV).2018:3-19.
[27]LUO Z,LI J,ZHU Y.A deep feature fusion network based on multiple attention mechanisms for joint iris-periocular biometric recognition[J].IEEE Signal Processing Letters,2021,28:1060-1064.
[28]YANG K,XU Z,FEI J.Dualsanet:Dual spatial attention network for iris recognition[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision.2021:889-897.
[29]VASWANI A,SHAZEER N,PARMAR N,et al.Attention isall you need[C]//Proceedings of the 31st International Confe-rence on Neural Information Processing Systems.2017:6000-60108.
[30]HOU Q,ZHOU D,FENG J.Coordinate attention for efficientmobile network design[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2021:13713-13722.
[31]SHI W,CABALLERO J,HUSZÁR F,et al.Real-time singleimage and video super-resolution using an efficient sub-pixel convolutional neural network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:1874-1883.
[32]ZHAO Y,PO L M,YAN Q,et al.Hierarchical regression network for spectral reconstruction from RGB images[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops.2020:422-423.
[33]CHEN H,HU G,LEI Z,et al.Attention-based two-stream convolutional networks for face spoofing detection[J].IEEE Transactions on Information Forensics and Security,2019,15:578-593.
[34]DENG J,GUO J,XUE N,et al.Arcface:Additive angular margin loss for deep face recognition[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2019:4690-469.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
地址：重庆市渝北区洪湖西路18号    邮编：401121  
电话：023-63500828（编辑部） 023-67039612（会议/专题） 023-67039623（财务/发票）
E-mail：jsjkx12@163.com
本系统由北京玛格泰克科技发展有限公司设计开发