Iris Image Classification Based on Hierarchical Visual Codebook
Iris Image Classification Based on Hierarchical Visual Codebook
复制标题
基于分层视觉码本的虹膜图像分类
DOI：
10.1109/tpami.2013.234
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发表时间：
2014-06-01
影响因子：
23.6
通讯作者：
Wang, Jianyu
中科院分区：
文献类型：
--
作者：
Sun, Zhenan;Zhang, Hui;Wang, Jianyu
虹膜识别作为一种可靠的个人身份识别方法已经得到了很好的研究，其目标是将每个虹膜图像的类别标签分配给唯一的主体。相反，虹膜图像分类旨在将虹膜图像分类到应用特定类别，例如，虹膜活性检测（真和假虹膜图像的分类），种族分类（例如，亚洲和非亚洲受试者的虹膜图像的分类）、由粗到细的虹膜识别（将中央数据库中的所有虹膜图像分类为多个类别）。提出了一种基于纹理分析的虹膜图像分类的通用框架。提出了一种新的虹膜纹理模式表示方法--层次视觉码本（HVC）。所提出的HVC方法是现有的两个词袋模型，即词汇树（VT）和局部约束线性编码（LLC）的集成。HVC采用由粗到细的视觉编码策略，并利用VT和LLC的优点，以准确和稀疏的虹膜纹理表示。大量的实验结果表明，所提出的虹膜图像分类方法实现了最先进的性能，虹膜活性检测，种族分类，从粗到细的虹膜识别。建立了一个模拟四种虹膜欺骗攻击的完整的假虹膜图像库，作为虹膜活性检测研究的基准。
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.