Sample reconstruction with deep autoencoder for one sample per person face recognition

One sample per person (OSPP) face recognition is a challenging problem in face recognition community. Lack of samples is the main reason for the failure of most algorithms in OSPP. In this study, the authors propose a new algorithm to generalise intra-class variations of multi-sample subjects to sin...

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Veröffentlicht in:	IET computer vision Jg. 11; H. 6; S. 471 - 478
Hauptverfasser:	Zhang, Yan, Peng, Hua
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	The Institution of Engineering and Technology 01.09.2017 Wiley
Schlagworte:	AR database CDA class-specific deep autoencoder CMU database extended Yale face database B face recognition image classification image coding image reconstruction image representation minimum L2 distance multisample subject to single-sample subject intraclass variations one sample per person face recognition OSPP face recognition principal component analysis principle component analysis regression analysis Research Article sample reconstruction softmax regression sparse represented-based classifier CDA one sample per person face recognition image classification AR database softmax regression class-specific deep autoencoder regression analysis sparse represented-based classifier minimum L2 distance image reconstruction CMU database extended Yale face database B principle component analysis image representation face recognition OSPP face recognition multisample subject to single-sample subject intraclass variations image coding sample reconstruction principal component analysis
ISSN:	1751-9632, 1751-9640, 1751-9640
Online-Zugang:	Volltext
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