Open AccessSample reconstruction with deep autoencoder for one sample per person face recognition
Author(s) -
Zhang Yan,
Peng Hua
Publication year - 2017
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2016.0322
Subject(s) - softmax function , autoencoder , artificial intelligence , pattern recognition (psychology) , facial recognition system , sample (material) , computer science , classifier (uml) , principal component analysis , face (sociological concept) , deep learning , social science , chemistry , chromatography , sociology
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.