An Improved Iris Recognition Algorithm Based on Hybrid Feature and ELM

2017 International Symposium on Application of Materials Science and Energy Materials

材料科学;能源学

Wang, Juan^1

School of Technology and Engineering, Xi'An Fanyi University, Taiyigong, Chang'an District, Xi'an

710105, China^1

关键词: 2-D Gabor filter;    Extreme learning machine;    High frequency HF;    Iris recognition algorithm;    Low intermediate frequencies;    Multi-granularity;    Real time performance;    Texture information;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/322/5/052030/pdf
DOI  :  10.1088/1757-899X/322/5/052030

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

The iris image is easily polluted by noise and uneven light. This paper proposed an improved extreme learning machine (ELM) based iris recognition algorithm with hybrid feature. 2D-Gabor filters and GLCM is employed to generate a multi-granularity hybrid feature vector. 2D-Gabor filter and GLCM feature work for capturing low-intermediate frequency and high frequency texture information, respectively. Finally, we utilize extreme learning machine for iris recognition. Experimental results reveal our proposed ELM based multi-granularity iris recognition algorithm (ELM-MGIR) has higher accuracy of 99.86%, and lower EER of 0.12% under the premise of real-time performance. The proposed ELM-MGIR algorithm outperforms other mainstream iris recognition algorithms.