Pseudo-full-space representation based classification for robust face recognition
作者:
Highlights:
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• PFSR is presented in an opposite way to sparse representation. The feasibility and stability of PFSRC are analyzed theoretically.
• CCR is designed to match with PSFR and complete the classification.
• CCI is defined to measure representation ability, CSI is defined to describe the category sparsity and reject outliers.
• PFSRC is based on utilizing existing available samples rather than constructing auxiliary training samples.
• PFSRC is competitive and robust for insufficient training samples FR problem.
摘要
•PFSR is presented in an opposite way to sparse representation. The feasibility and stability of PFSRC are analyzed theoretically.•CCR is designed to match with PSFR and complete the classification.•CCI is defined to measure representation ability, CSI is defined to describe the category sparsity and reject outliers.•PFSRC is based on utilizing existing available samples rather than constructing auxiliary training samples.•PFSRC is competitive and robust for insufficient training samples FR problem.
论文关键词:Sparse representation,Pseudo-full-space representation,Category concentration index,Category contribution rate,Face recognition
论文评审过程:Received 30 March 2017, Revised 1 August 2017, Accepted 13 September 2017, Available online 25 September 2017, Version of Record 9 October 2017.
论文官网地址:https://doi.org/10.1016/j.image.2017.09.006