Institutional Repository
| Iris image classification based on hierarchical visual codebook | |
| Sun, Zhenan (1); Zhang, Hui (2); Tan, Tieniu (1); Wang, Jianyu (3); Zhang, H.(zhanghui@iscas.ac.cn) | |
| 2014 | |
| Source | IEEE Transactions on Pattern Analysis and Machine Intelligence
 |
| ISSN | 1628828 |
| Volume | 36Issue:6Pages:1120-1133 |
| English Abstract | Iris recognition as a reliable method for personal identification has been well-studied with the objective to assign the class label of each iris image to a unique subject. In contrast, iris image classification aims to classify an iris image to an application specific category, e.g., iris liveness detection (classification of genuine and fake iris images), race classification (e.g., classification of iris images of Asian and non-Asian subjects), coarse-to-fine iris identification (classification of all iris images in the central database into multiple categories). This paper proposes a general framework for iris image classification based on texture analysis. A novel texture pattern representation method called Hierarchical Visual Codebook (HVC) is proposed to encode the texture primitives of iris images. The proposed HVC method is an integration of two existing Bag-of-Words models, namely Vocabulary Tree (VT), and Locality-constrained Linear Coding (LLC). The HVC adopts a coarse-to-fine visual coding strategy and takes advantages of both VT and LLC for accurate and sparse representation of iris texture. Extensive experimental results demonstrate that the proposed iris image classification method achieves state-of-the-art performance for iris liveness detection, race classification, and coarse-to-fine iris identification. A comprehensive fake iris image database simulating four types of iris spoof attacks is developed as the benchmark for research of iris liveness detection. © 2013 IEEE.; Iris recognition as a reliable method for personal identification has been well-studied with the objective to assign the class label of each iris image to a unique subject. In contrast, iris image classification aims to classify an iris image to an application specific category, e.g., iris liveness detection (classification of genuine and fake iris images), race classification (e.g., classification of iris images of Asian and non-Asian subjects), coarse-to-fine iris identification (classification of all iris images in the central database into multiple categories). This paper proposes a general framework for iris image classification based on texture analysis. A novel texture pattern representation method called Hierarchical Visual Codebook (HVC) is proposed to encode the texture primitives of iris images. The proposed HVC method is an integration of two existing Bag-of-Words models, namely Vocabulary Tree (VT), and Locality-constrained Linear Coding (LLC). The HVC adopts a coarse-to-fine visual coding strategy and takes advantages of both VT and LLC for accurate and sparse representation of iris texture. Extensive experimental results demonstrate that the proposed iris image classification method achieves state-of-the-art performance for iris liveness detection, race classification, and coarse-to-fine iris identification. A comprehensive fake iris image database simulating four types of iris spoof attacks is developed as the benchmark for research of iris liveness detection. © 2013 IEEE. |
| Indexed Type | SCI ; EI |
| Keyword | Iris Image Classification Hierarchical Visual Codebook (Hvc) Iris Liveness Detection Race Classification Coarse-to-fine Iris Identification |
| Department | (1) Center for Research on Intelligent Perception and Computing (CRIPAC), National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Beijing 100190, China; (2) Institute of Software, Chinese Academy of Sciences, Beijing 100190, China; (3) Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China |
| Language | 英语 |
| WOS ID | WOS:000337124200006 |
| Citation statistics | |
| Content Type | 期刊论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/16857 |
| Collection | 中国科学院软件研究所 |
| Corresponding Author | Zhang, H.(zhanghui@iscas.ac.cn) |
| Recommended Citation GB/T 7714 | Sun, Zhenan ,Zhang, Hui ,Tan, Tieniu ,et al. Iris image classification based on hierarchical visual codebook[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2014,36(6):1120-1133. |
| APA | Sun, Zhenan ,Zhang, Hui ,Tan, Tieniu ,Wang, Jianyu ,&Zhang, H..(2014).Iris image classification based on hierarchical visual codebook.IEEE Transactions on Pattern Analysis and Machine Intelligence,36(6),1120-1133. |
| MLA | Sun, Zhenan ,et al."Iris image classification based on hierarchical visual codebook".IEEE Transactions on Pattern Analysis and Machine Intelligence 36.6(2014):1120-1133. |
| Files in This Item: | There are no files associated with this item. | |||||
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment