Title:
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From emotions to action units with hidden and semi-hidden-task learning
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Author:
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Ruiz, Adrià; Van de Weijer, Joost; Binefa i Valls, Xavier
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Abstract:
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Comunicació presentada a: IEEE International Conference on Computer Vision (ICCV 2015) celebrat a Santiago, Xile, de l'11 al 18 de desembre de 2015. |
Abstract:
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Limited annotated training data is a challenging problem
in Action Unit recognition. In this paper, we investigate
how the use of large databases labelled according to the
6 universal facial expressions can increase the generalization
ability of Action Unit classifiers. For this purpose, we
propose a novel learning framework: Hidden-Task Learning.
HTL aims to learn a set of Hidden-Tasks (Action Units)
for which samples are not available but, in contrast, training
data is easier to obtain from a set of related Visible-
Tasks (Facial Expressions). To that end, HTL is able to exploit
prior knowledge about the relation between Hidden
and Visible-Tasks. In our case, we base this prior knowledge
on empirical psychological studies providing statistical
correlations between Action Units and universal facial
expressions. Additionally, we extend HTL to Semi-Hidden
Task Learning (SHTL) assuming that Action Unit training
samples are also provided. Performing exhaustive experiments
over four different datasets, we show that HTL and
SHTL improve the generalization ability of AU classifiers by
training them with additional facial expression data. Additionally,
we show that SHTL achieves competitive performance
compared with state-of-the-art Transductive Learning
approaches which face the problem of limited training
data by using unlabelled test samples during training. |
Abstract:
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This paper is part of a project that has received funding
from the European Union’s Horizon 2020 research and
innovation programme under grant agreement No 645012.
Adria Ruiz and Xavier Binefa would also like to acknowledge
Spanish Government to provide support under grants
CICYT TIN2012-39203 and FPU13/01740. Joost van de
Weijer acknowledges Project TIN2013-41751 of the Spanish
Ministry of Science and the Generalitat de Catalunya
Project under Grant 2014-SGR-221. |
Subject(s):
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-Training -Gold -Training data -Face recognition -Databases -Psychology -Hidden Markov models |
Rights:
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© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
The final published article can be found at http://ieeexplore.ieee.org/document/7410779/ |
Document type:
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Conference Object Article - Accepted version |
Published by:
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Institute of Electrical and Electronics Engineers (IEEE)
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