An Improved Iris Recognition Algorithm Based on Hybrid Feature and ELM

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.

作者: Juan Wang

作者单位: School of Technology and Engineering,Xian Fanyi University,Taiyigong,Changan District,Xian 710105,P.R.China

会议类型: 国际会议

会议名称: 2017 International Symposium on Application of Materials Science and Energy Materials (SAMSE 2017) (2017材料科学应用与能源材料国际研讨会)

会议地点: 上海

会议语种:英文

页码: 1-5

在线出版日期: 2017-12-28（万方平台首次上网日期，不代表论文的发表时间）