Imperial College London
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STEFANOS ZAFEIRIOU, PhD

Faculty of Engineering, Department of Computing

Professor in Machine Learning & Computer Vision
 
 
 
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Contact

 

+44 (0)20 7594 8461s.zafeiriou Website CV

 
 
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Location

 

375Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Sagonas and Ververas and Panagakis and Zafeiriou:2018:10.1109/TPAMI.2017.2784421,
author = {Sagonas and Ververas and Panagakis and Zafeiriou, S},
doi = {10.1109/TPAMI.2017.2784421},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
pages = {2668--2681},
title = {Recovering joint and individual components in facial data},
url = {http://dx.doi.org/10.1109/TPAMI.2017.2784421},
volume = {40},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - A set of images depicting faces with different expressions or in various ages consists of components that are shared across all images (i.e., joint components) imparting to the depicted object the properties of human faces as well as individual components that are related to different expressions or age groups. Discovering the common (joint) and individual components in facial images is crucial for applications such as facial expression transfer and age progression. The problem is rather challenging when dealing with images captured in unconstrained conditions in the presence of sparse non-Gaussian errors of large magnitude (i.e., sparse gross errors or outliers) and contain missing data. In this paper, we investigate the use of a method recently introduced in statistics, the so-called Joint and Individual Variance Explained (JIVE) method, for the robust recovery of joint and individual components in visual facial data consisting of an arbitrary number of views. Since the JIVE is not robust to sparse gross errors, we propose alternatives, which are (1) robust to sparse gross, non-Gaussian noise, (2) able to automatically find the individual components rank, and (3) can handle missing data. We demonstrate the effectiveness of the proposed methods to several computer vision applications, namely facial expression synthesis and 2D and 3D face age progression ‘in-the-wild’.
AU  - Sagonas
AU  - Ververas
AU  - Panagakis
AU  - Zafeiriou,S
DO  - 10.1109/TPAMI.2017.2784421
EP  - 2681
PY  - 2018///
SN  - 0162-8828
SP  - 2668
TI  - Recovering joint and individual components in facial data
T2  - IEEE Transactions on Pattern Analysis and Machine Intelligence
UR  - http://dx.doi.org/10.1109/TPAMI.2017.2784421
UR  - http://hdl.handle.net/10044/1/52825
VL  - 40
ER  -
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