Direct Discriminant Analysis Using Volterra Kernels for Face Recognition

摘要：

　　Based on non-linear Volterra kernels mapping and direct discrimination analysis(DD-Volterra),a novel face recognition algorithm is proposed.Firstly,the original image is segmented into specific sub blocks and seeks functional mapping using truncated Volterra kernels.Next,simultaneous diagonalization obtain Volterra kernel optimal projection matrix.This matrix can discard useless information that exist in the null space of the inter-class.Also,it can reserve discriminative information that exist in the null space of the intra-class.Finally,in the test,each block of the test image is classified separately,voting strategy and nearest neighbor classifier algorithm are used for classification.Experiments show that the proposed DD-Volterra method has better performance for it is more effective than Volterrafaces during the extracting facial feature stage.

关键词： Face recognition Feature extraction Volterra kernels Direct discriminant analysis

作者: Guang Feng Hengjian Li Jiwen Dong Jiashu Zhang

作者单位: Shandong Provincial Key Laboratory of Network Based Intelligent Computing,University of Jinan,Jinan Sichuan Key Laboratory of Signal and Information Processing,Southwest Jiaotong University,Chengdu 61

会议类型: 国际会议

会议名称: 第七届全国模式识别学术会议(The 7th Chinese Conference on Pattern Recognition,CCPR2016)

会议地点: 成都

会议语种:英文

页码: 404-412

在线出版日期: 2016-11-03（万方平台首次上网日期，不代表论文的发表时间）