An Effective Feature Descriptor with Gabor Filter and Uniform Local Binary Pattern Transcoding for Iris Recognition

作     者：Huo, Guang Guo, Huan Zhang, Yangrui Zhang, Qi Li, Wenyu Li, Bin 

作者机构：Northeast Elect Power Univ Sch Comp Sci Jilin 132012 Jilin Peoples R China Northeast Elect Power Univ Sch Foreign Languages Jilin 132012 Jilin Peoples R China 

出 版 物：《PATTERN RECOGNITION AND IMAGE ANALYSIS》 (模式识别与图形分析)

年 卷 期：2019年第29卷第4期

页      面：688-694页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：science and technology project of the Jilin Provincial Education Department [JJKH20180448KJ] science and technology development plan project of Jilin Province [20180520017JH] PhD Start-up Foundation of NEEPU 

主　　题：biometric security iris recognition Gabor filter uniform local binary pattern 

摘      要：Iris recognition is recognized as one of the most reliable and efficient technique for human identification in the biometric fields. The Gabor filter and local binary pattern (LBP) are widely adopted for feature extraction in face recognition. However, it is difficult to achieve high recognition accuracy when the Gabor filter or LBP is directly applied to iris texture representation. This paper presents an effective iris feature descriptor, which first uses 2D-Gabor filter to extract multi-orientation imaginary (MOI) feature, and then applies uniform LBP for region feature encoding. Thus, the MOI feature-by-point energy is converted into that of the uniform LBP histogram-by-block, during which the distributions of the intra- and inter-class are greatly widened. Such process largely improves distinguishability of MOI features. Finally, the Bhattacharyya distance is adopted for matching. Experimental results on CASIA and JLU iris image databases show that this method performs better for combining MOI features and LBP encoding as compared to their individual function.