Imperial College London

STEFANOS ZAFEIRIOU, PhD

Faculty of EngineeringDepartment of Computing

Reader in Machine Learning and 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{Moschoglou:2018:10.1109/JSTSP.2018.2877108,
author = {Moschoglou, S and Ververas, E and Panagakis, Y and Nicolaou, MA and Zafeiriou, S},
doi = {10.1109/JSTSP.2018.2877108},
journal = {IEEE Journal of Selected Topics in Signal Processing},
pages = {1324--1337},
title = {Multi-attribute robust component analysis for facial UV maps},
url = {http://dx.doi.org/10.1109/JSTSP.2018.2877108},
volume = {12},
year = {2018}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - The collection of large-scale three-dimensional (3-D) face models has led to significant progress in the field of 3-D face alignment “in-the-wild,” with several methods being proposed toward establishing sparse or dense 3-D correspondences between a given 2-D facial image and a 3-D face model. Utilizing 3-D face alignment improves 2-D face alignment in many ways, such as alleviating issues with artifacts and warping effects in texture images. However, the utilization of 3-D face models introduces a new set of challenges for researchers. Since facial images are commonly captured in arbitrary recording conditions, a considerable amount of missing information and gross outliers is observed (e.g., due to self-occlusion, subjects wearing eye-glasses, and so on). To this end, in this paper we propose the Multi-Attribute Robust Component Analysis (MA-RCA), a novel technique that is suitable for facial UV maps containing a considerable amount of missing information and outliers, while additionally, elegantly incorporates knowledge from various available attributes, such as age and identity. We evaluate the proposed method on problems such as UV denoising, UV completion, facial expression synthesis, and age progression, where MA-RCA outperforms compared techniques.
AU - Moschoglou,S
AU - Ververas,E
AU - Panagakis,Y
AU - Nicolaou,MA
AU - Zafeiriou,S
DO - 10.1109/JSTSP.2018.2877108
EP - 1337
PY - 2018///
SN - 1932-4553
SP - 1324
TI - Multi-attribute robust component analysis for facial UV maps
T2 - IEEE Journal of Selected Topics in Signal Processing
UR - http://dx.doi.org/10.1109/JSTSP.2018.2877108
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000454221700017&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - http://hdl.handle.net/10044/1/69748
VL - 12
ER -