CASIA OpenIR  > 智能感知与计算研究中心
DeepIris: Learning pairwise filter bank for heterogeneous iris verification
Liu, Nianfeng; Zhang, Man; Li, Haiqing; Sun, Zhenan; Tan, Tieniu
Source PublicationPATTERN RECOGNITION LETTERS
2016-10-15
Volume82Issue:2Pages:154-161
SubtypeArticle
AbstractHeterogeneous iris recognition (HIR) is in great demand for a large-scale identity management system. Iris images acquired in heterogeneous environment have large intra-class variations, such as different resolutions or different sensor optics, etc. Therefore, it is challenging to manually design a robust encoding filter to face the complex intra-class variations of heterogeneous iris images. This paper proposes a deep learning based framework for heterogeneous iris verification, namely DeepIris, which learns relational features to measure the similarity between pairs of iris images based on convolutional neural networks. DeepIris is a novel solution to iris recognition in two main aspects. (1) DeepIris learns a pairwise filter bank to establish the relationship between heterogeneous iris images, where pairs of filters are learned from two heterogeneous sources. (2) Different from two separate steps in terms of handcrafted feature extraction and feature matching in conventional solutions, DeepIris directly learns a nonlinear mapping function between pairs of iris images and their identity supervision with a pairwise filter bank (PFB) from different sources. Thus, the learned pairwise filters can adapt to new sources when given new training data. Extensive experimental results on the Q-FIRE and the CASIA cross sensor datasets demonstrate that EER (Equal Error Rate) of heterogeneous iris verification is reduced by 90% using DeepIris compared to traditional methods. (C) 2015 Elsevier B.V. All rights reserved.
KeywordBiometrics Iris Verification Convolutional Neural Networks Deep Learning Iris Recognition
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.patrec.2015.09.016
WOS KeywordIMAGES
Indexed BySCI
Language英语
Funding OrganizationNational Basic Research Program of China(2012CB316300) ; National Natural Science Foundation of China(61273272) ; Beijing Baidu Netcom Science Technology Co., Ltd.
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000386874600008
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13622
Collection智能感知与计算研究中心
AffiliationChinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Ctr Res Intelligent Percept & Comp, Natl Lab Pattern Recognit,Inst Automat, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Liu, Nianfeng,Zhang, Man,Li, Haiqing,et al. DeepIris: Learning pairwise filter bank for heterogeneous iris verification[J]. PATTERN RECOGNITION LETTERS,2016,82(2):154-161.
APA Liu, Nianfeng,Zhang, Man,Li, Haiqing,Sun, Zhenan,&Tan, Tieniu.(2016).DeepIris: Learning pairwise filter bank for heterogeneous iris verification.PATTERN RECOGNITION LETTERS,82(2),154-161.
MLA Liu, Nianfeng,et al."DeepIris: Learning pairwise filter bank for heterogeneous iris verification".PATTERN RECOGNITION LETTERS 82.2(2016):154-161.
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