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The Many Moods of Emotion

Valentin Vielzeuf 1, 2 Corentin Kervadec 1 Stéphane Pateux 1 Frédéric Jurie 2
2 Equipe Image - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image et Instrumentation de Caen
Abstract : This paper presents a novel approach to the facial expression generation problem. Building upon the assumption of the psychological community that emotion is intrinsically continuous, we first design our own continuous emotion representation with a 3-dimensional latent space issued from a neural network trained on discrete emotion classification. The so-obtained representation can be used to annotate large in the wild datasets and later used to trained a Generative Adversarial Network. We first show that our model is able to map back to discrete emotion classes with a objectively and subjectively better quality of the images than usual discrete approaches. But also that we are able to pave the larger space of possible facial expressions, generating the many moods of emotion. Moreover, two axis in this space may be found to generate similar expression changes as in traditional continuous representations such as arousal-valence. Finally we show from visual interpretation, that the third remaining dimension is highly related to the well-known dominance dimension from psychology.
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Contributor : Valentin Vielzeuf Connect in order to contact the contributor
Submitted on : Wednesday, October 31, 2018 - 10:05:52 AM
Last modification on : Wednesday, November 3, 2021 - 5:13:29 AM
Long-term archiving on: : Friday, February 1, 2019 - 12:20:42 PM


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  • HAL Id : hal-01908390, version 1
  • ARXIV : 1810.13197


Valentin Vielzeuf, Corentin Kervadec, Stéphane Pateux, Frédéric Jurie. The Many Moods of Emotion. 2018. ⟨hal-01908390⟩



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