Title:
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Fusion of valence and arousal annotations through dynamic
subjective ordinal modelling
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Author:
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Ruiz, Adrià; Martinez, Oriol; Binefa i Valls, Xavier; Sukno, Federico Mateo
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Abstract:
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Comunicació presentada a: FG 2017 12th IEEE International Conference on Automatic Face and Gesture Recognition, celebrada del 30 de maig al 3 de juny de 2017 a Washington, Estats Units d'Amèrica. |
Abstract:
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An essential issue when training and validating
computer vision systems for affect analysis is how to obtain
reliable ground-truth labels from a pool of subjective annotations.
In this paper, we address this problem when labels are
given in an ordinal scale and annotated items are structured
as temporal sequences. This problem is of special importance
in affective computing, where collected data is typically formed
by videos of human interactions annotated according to the
Valence and Arousal (V-A) dimensions. Moreover, recent works
have shown that inter-observer agreement of V-A annotations
can be considerably improved if these are given in a discrete
ordinal scale. In this context, we propose a novel framework
which explicitly introduces ordinal constraints to model the
subjective perception of annotators. We also incorporate dynamic
information to take into account temporal correlations
between ground-truth labels. In our experiments over synthetic
and real data with V-A annotations, we show that the proposed
method outperforms alternative approaches which do not take
into account either the ordinal structure of labels or their
temporal correlation. |
Abstract:
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This work is partly supported by the Spanish Ministry
of Economy and Competitiveness under the Ramon y Cajal
fellowships and the Maria de Maeztu Units of Excellence
Programme (MDM-2015-0502), and the Kristina project
funded by the European Union Horizon 2020 research and
innovation programme under grant agreement No 645012.
Adria Ruiz would also like to acknowledge Spanish Government
to provide support under grant FPU13/01740. |
Subject(s):
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-Observers -Labeling -Context -Training -Affective computing -Videos -Computer vision |
Rights:
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© 2017 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/7961760 |
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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