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基于光谱独立分量分析的新型抗伪造虹膜识别方案，以应对隐形眼镜攻击。

A Novel Anti-Spoofing Solution for Iris Recognition Toward Cosmetic Contact Lens Attack Using Spectral ICA Analysis.

机构信息

Department of Photonics, National Chiao Tung University, 1001 University Road, Hsinchu 30010, Taiwan.

Department of Computer Science & Information Engineering, National Central University, 300 Zhongda Road, Zhongli District, Taoyuan 32001, Taiwan.

出版信息

Sensors (Basel). 2018 Mar 6;18(3):795. doi: 10.3390/s18030795.

DOI:10.3390/s18030795
PMID:29509692
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5876747/
Abstract

In this study, we maneuvered a dual-band spectral imaging system to capture an iridal image from a cosmetic-contact-lens-wearing subject. By using the independent component analysis to separate individual spectral primitives, we successfully distinguished the natural iris texture from the cosmetic contact lens (CCL) pattern, and restored the genuine iris patterns from the CCL-polluted image. Based on a database containing 200 test image pairs from 20 CCL-wearing subjects as the proof of concept, the recognition accuracy (False Rejection Rate: FRR) was improved from FRR = 10.52% to FRR = 0.57% with the proposed ICA anti-spoofing scheme.

摘要

在这项研究中，我们操纵一个双波段光谱成像系统，从一个戴美容隐形眼镜的对象捕获虹膜图像。通过使用独立成分分析来分离单个光谱基元，我们成功地将自然虹膜纹理与美容隐形眼镜（CCL）图案区分开来，并从 CCL 污染的图像中恢复真实的虹膜图案。基于一个包含 200 对来自 20 个戴 CCL 受试者的测试图像的数据库作为概念验证，所提出的 ICA 防欺骗方案将识别准确率（误拒绝率：FRR）从 FRR = 10.52%提高到 FRR = 0.57%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f37/5876747/78469284e723/sensors-18-00795-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f37/5876747/8297a1c584de/sensors-18-00795-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f37/5876747/e9bbf2af3701/sensors-18-00795-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f37/5876747/3abf1a12b773/sensors-18-00795-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f37/5876747/849a87317c18/sensors-18-00795-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f37/5876747/50cce123f1ce/sensors-18-00795-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f37/5876747/ad905502eccc/sensors-18-00795-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f37/5876747/d0e32fd36d5e/sensors-18-00795-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f37/5876747/78469284e723/sensors-18-00795-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f37/5876747/8297a1c584de/sensors-18-00795-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f37/5876747/e9bbf2af3701/sensors-18-00795-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f37/5876747/3abf1a12b773/sensors-18-00795-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f37/5876747/849a87317c18/sensors-18-00795-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f37/5876747/50cce123f1ce/sensors-18-00795-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f37/5876747/ad905502eccc/sensors-18-00795-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f37/5876747/d0e32fd36d5e/sensors-18-00795-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f37/5876747/78469284e723/sensors-18-00795-g008.jpg

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本文引用的文献

1
Iris Image Classification Based on Hierarchical Visual Codebook.基于分层视觉代码本的虹膜图像分类。
IEEE Trans Pattern Anal Mach Intell. 2014 Jun;36(6):1120-33. doi: 10.1109/TPAMI.2013.234.
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