Iris Recogniton Based on Fearure Extraction in Kernel Space

摘要：

Iris-based recognition approach is a popular and efficient method in personal identification field. How to code an iris image is the key issue for iris recognition. In this paper, we will apply Kernel-based nonlinear feature extraction Kernel Principal Component Analysis (KPCA), Kernel Independent Component Analysis (KICA), Kernel Linear Discriminant Analysis (KLDA), and Kernel Springy Discriminant Analysis (KSDA) to encode an iris image. The idea of Kernel-based feature extraction methods is to map the input data into an implicit feature space F with the kernel trick firstly, and then perform original linear methods to produce nonlinear projection matrix of input data.The performances of these encoding methods are analyzed using CASIAII database. We plot a series of Receiver Operating Characteristics (ROCs) and Equal Error Rate (EER) to demonstrate various the different performances of different encoding methods.

作者: Shuai Shao Mei Xie

作者单位: Institute of Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P.R.China

会议类型: 国际会议

会议名称: 2007年通信、电路与系统国际会议(2007 International Conference on Communications,Circuits and Systems Proceedings)

会议地点: 日本福冈

会议语种:英文

在线出版日期: 2007-07-11（万方平台首次上网日期，不代表论文的发表时间）