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

• Published in: IET computer vision Vol. 11; no. 6; pp. 471 - 478
• Main Authors: Zhang, Yan, Peng, Hua
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.09.2017; Wiley
• Subjects: 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

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...