| Combining 2D texture and 3D geometry features for Reliable iris presentation attack detection using light field focal stack | |
Luo, Zhengquan1; Wang, Yunlong2 ; Liu, Nianfeng2; Wang, Zilei1
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| Source Publication | IET BIOMETRICS
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| ISSN | 2047-4938 |
| 2022-08-27 | |
| Pages | 10 |
| Corresponding Author | Wang, Yunlong(yunlongwang@cnpac.ia.ac.cn) |
| Abstract | 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 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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] |
| Funding Organization | CAAI Huawei MindSpore Open Fund ; National Natural Science Foundation of China ; Strategic Priority Research Program of Chinese Academy of Sciences |
| WOS Research Area | Computer Science |
| WOS Subject | Computer Science, Artificial Intelligence |
| WOS ID | WOS:000846483600001 |
| Publisher | WILEY |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/50063 |
| Collection | 智能感知与计算 |
| Corresponding Author | Wang, Yunlong |
| Affiliation | 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 |
| Corresponding Author Affilication | Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| Recommended Citation 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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