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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Bibliographic Details
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
ISSN:1751-9632, 1751-9640, 1751-9640
Online Access:Get full text
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Description
Summary: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 single-sample subjects by deep autoencoder and reconstruct new samples. In the proposed algorithm, a generalised deep autoencoder is first trained with all images in the gallery, then a class-specific deep autoencoder (CDA) is fine-tuned for each single-sample subject with its single sample. Samples of the multi-sample subject, which is most like the single-sample subject, are input to the corresponding CDA to reconstruct new samples. For classification, minimum L2 distance, principle component analysis, sparse represented-based classifier and softmax regression are used. Experiments on the Extended Yale Face Database B, AR database and CMU PIE database are provided to show the validity of the proposed algorithm.
ISSN:1751-9632
1751-9640
1751-9640
DOI:10.1049/iet-cvi.2016.0322