Imperial College London

STEFANOS ZAFEIRIOU, PhD

Faculty of EngineeringDepartment of Computing

Professor in Machine Learning & Computer Vision
 
 
 
//

Contact

 

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

 
 
//

Location

 

375Huxley BuildingSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Tzimiropoulos:2013:10.1007/978-3-642-37431-9_50,
author = {Tzimiropoulos, G and Alabort-I-Medina, J and Zafeiriou, S and Pantic, M},
doi = {10.1007/978-3-642-37431-9_50},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
pages = {650--663},
title = {Generic active appearance models revisited},
url = {http://dx.doi.org/10.1007/978-3-642-37431-9_50},
volume = {7726 LNCS},
year = {2013}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - The proposed Active Orientation Models (AOMs) are generative models of facial shape and appearance. Their main differences with the well-known paradigm of Active Appearance Models (AAMs) are (i) they use a different statistical model of appearance, (ii) they are accompanied by a robust algorithm for model fitting and parameter estimation and (iii) and, most importantly, they generalize well to unseen faces and variations. Their main similarity is computational complexity. The project-out version of AOMs is as computationally efficient as the standard project-out inverse compositional algorithm which is admittedly the fastest algorithm for fitting AAMs. We show that not only does the AOM generalize well to unseen identities, but also it outperforms state-of-the-art algorithms for the same task by a large margin. Finally, we prove our claims by providing Matlab code for reproducing our experiments ( http://ibug.doc.ic.ac.uk/resources ). © 2013 Springer-Verlag.
AU - Tzimiropoulos,G
AU - Alabort-I-Medina,J
AU - Zafeiriou,S
AU - Pantic,M
DO - 10.1007/978-3-642-37431-9_50
EP - 663
PY - 2013///
SN - 0302-9743
SP - 650
TI - Generic active appearance models revisited
T2 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
UR - http://dx.doi.org/10.1007/978-3-642-37431-9_50
VL - 7726 LNCS
ER -