Palmprint recognition using dual-tree complex wavelet transform and compressed sensing

摘要：

In this paper, based on the dual-tree complex wavelet transform (DT-CWT) and compressed sensing (CS), a novel and high palmprint recognition performance algorithm is proposed. Firstly. DT-CWT, which provide both approximate shift invariance and good directional selectivity, is employed to represent the palmprint image with better preserving the discriminable features with less redundant and computationally efficient. Then the PCA (Principal Component Analysis), based on linearly projecting the image subband coefficients space to a low dimensional feature subspace, is employed to extract the feature of the palmprint images. At last, the robust compressed sensing classification algorithm is used to distinguish the palmprint images from different hands. The experimental results carried on PolyU palmprint database show that the proposed algorithm has better recognition performance than traditional Nearest Neighbor Classification algorithm.

关键词： palmprint recognition DT-CWT PCA Compressed Sensing

作者: Hengjian Li Lianhai Wang

作者单位: Shandong Provincial Key Laboratory of computer Network, Shandong Computer Science Center, Jinan, 250014, China

会议类型: 国际会议

会议名称: 2012 International Conference on Measurement,Information and Control(2012测量、信息与控制国际会议 ICMIC2012)

会议地点: 哈尔滨

会议语种:英文

页码: 563-567

在线出版日期: 2012-05-18（万方平台首次上网日期，不代表论文的发表时间）