会议专题

Cost-Sensitive Sparsity Preserving Projections for Face Recognition

  As one of the most popular research topics,sparse representation (SR) technique has been successfully employed to solve face recognition task.Though current SR based methods prove to achieve high classification accuracy,they implicitly assume that the losses of all misclassifications are the same.However,in many real-world face recognition applications,this assumption may not hold as different misclassifications could lead to different losses.Driven by this concern,in this paper,we propose a cost-sensitive sparsity preserving projections (CSSPP) for face recognition.CSSPP considers the cost information of sparse representation while calculating the sparse structure of the training set.Then,CSSPP employs the sparsity preserving projection method to achieve the projection transform and keeps the sparse structure in the low-dimensional space.Experimental results on the public AR and FRGC face databases are presented to demonstrate that both of the proposed approaches can achieve high recognition rate and low misclassification loss,which validate the efficacy of the proposed approach.

cost-sensitive learning sparse representation cost-sensitive classifier feature extraction face recognition

Xiaoyuan Jing Wenqian Li Hao Gao Yongfang Yao Jiangyue Man

College of Automation,Nanjing University of Posts and Telecommunications,Nanjing,China;State Key Lab College of Automation,Nanjing University of Posts and Telecommunications,Nanjing,China

国际会议

2013 2nd international Conference on Opto-Electronics Engineering and Materials Eesearch(2013第二届光电工程与材料研究国际会议)(OEMR2013)

郑州

英文

1153-1158

2013-10-19(万方平台首次上网日期,不代表论文的发表时间)