Combining 2D texture and 3D geometry features for Reliable iris presentation attack detection using light field focal stack | |
Luo, Zhengquan1; Wang, Yunlong2![]() ![]() | |
发表期刊 | IET BIOMETRICS
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ISSN | 2047-4938 |
2022-08-27 | |
页码 | 10 |
摘要 | Iris presentation attack detection (PAD) is still an unsolved problem mainly due to the various spoof attack strategies and poor generalisation on unseen attackers. In this paper, the merits of both light field (LF) imaging and deep learning (DL) are leveraged to combine 2D texture and 3D geometry features for iris liveness detection. By exploring off-the-shelf deep features of planar-oriented and sequence-oriented deep neural networks (DNNs) on the rendered focal stack, the proposed framework excavates the differences in 3D geometric structure and 2D spatial texture between bona fide and spoofing irises captured by LF cameras. A group of pre-trained DL models are adopted as feature extractor and the parameters of SVM classifiers are optimised on a limited number of samples. Moreover, two-branch feature fusion further strengthens the framework's robustness and reliability against severe motion blur, noise, and other degradation factors. The results of comparative experiments indicate that variants of the proposed framework significantly surpass the PAD methods that take 2D planar images or LF focal stack as input, even recent state-of-the-art (SOTA) methods fined-tuned on the adopted database. Presentation attacks, including printed papers, printed photos, and electronic displays, can be accurately detected without fine-tuning a bulky CNN. In addition, ablation studies validate the effectiveness of fusing geometric structure and spatial texture features. The results of multi-class attack detection experiments also verify the good generalisation ability of the proposed framework on unseen presentation attacks. |
DOI | 10.1049/bme2.12092 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | CAAI Huawei MindSpore Open Fund[CAAIXSJLJJ-2021-053A] ; National Natural Science Foundation of China[61906199] ; National Natural Science Foundation of China[62006225] ; National Natural Science Foundation of China[62176025] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA27040700] |
项目资助者 | CAAI Huawei MindSpore Open Fund ; National Natural Science Foundation of China ; Strategic Priority Research Program of Chinese Academy of Sciences |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:000846483600001 |
出版者 | WILEY |
是否为代表性论文 | 否 |
七大方向——子方向分类 | 生物特征识别 |
国重实验室规划方向分类 | 视觉信息处理 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/50063 |
专题 | 模式识别实验室 |
通讯作者 | Wang, Yunlong |
作者单位 | 1.Univ Sci & Technol China, Dept Automat, Hefei, Anhui, Peoples R China 2.Chinese Acad Sci CASIA, Ctr Res Intelligent Percept & Comp CRIPAC, Natl Lab Pattem Recognit NLPR, Inst Automat, 95 Zhongguancun East St, Beijing 100190, Peoples R China |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Luo, Zhengquan,Wang, Yunlong,Liu, Nianfeng,et al. Combining 2D texture and 3D geometry features for Reliable iris presentation attack detection using light field focal stack[J]. IET BIOMETRICS,2022:10. |
APA | Luo, Zhengquan,Wang, Yunlong,Liu, Nianfeng,&Wang, Zilei.(2022).Combining 2D texture and 3D geometry features for Reliable iris presentation attack detection using light field focal stack.IET BIOMETRICS,10. |
MLA | Luo, Zhengquan,et al."Combining 2D texture and 3D geometry features for Reliable iris presentation attack detection using light field focal stack".IET BIOMETRICS (2022):10. |
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