Iris Image Classification Based on Hierarchical Visual Codebook

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 36; no. 6; pp. 1120 - 1133
• Main Authors: Sun, Zhenan, Zhang, Hui, Tan, Tieniu, Wang, Jianyu
• Format: Journal Article
• Language: English
• Published: Los Alamitos, CA IEEE 01.06.2014; IEEE Computer Society; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Artificial intelligence; Biomedical imaging; Biometric Identification - methods; Classification; Coarse-to-fine iris identification; Coding, codes; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Databases, Factual; Encoding; Ethnic iris classification; Exact sciences and technology; Feature extraction; Hierarchical Visual Codebook (HVC); Humans; Image classification; Image contrast; Image detection; Image Processing, Computer-Assisted - methods; Information, signal and communications theory; Iris; Iris - anatomy & histology; Iris image classification; Iris liveness detection; Iris recognition; Memory and file management (including protection and security); Memory organisation. Data processing; Pattern recognition. Digital image processing. Computational geometry; Signal and communications theory; Software; Surface layer; Telecommunications and information theory; Texture; Visual; Visualization; Vocabulary; Iris image classification; Hierarchical Visual Codebook (HVC); coarse-to-fine iris identification; iris liveness detection; race classification; Biometrics; Vocabulary; Image processing; Distributed database; Liveness; Bag of words; Linear coding; Codebook; Sparse representation; Forgery; Computer security; Image contrast; Computer vision; Image texture; Iris (eye); Hierarchical classification; Image databank; Information retrieval; Multidatabase retrieval; Tree structure; Experimental result; Image classification
• ISSN: 0162-8828; 1939-3539; 2160-9292; 1939-3539
• DOI: 10.1109/TPAMI.2013.234

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.