Perceptual facial expression representation

Olga Mikheeva, Carl Henrik Ek, Hedvig Kjellstroem

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

Abstract

Dissimilarity measures are often used as a proxy or a handle to reason about data. This can be problematic, as the data representation is often a consequence of the capturing process or how the data is visualized, rather than a reflection of the semantics that we want to extract. Facial expressions are a subtle and essential part of human communication but they are challenging to extract from current representations. In this paper we present a method that is capable of learning semantic representations of faces in a data driven manner. Our approach uses sparse human supervision which our method grounds in the data. We provide experimental justification of our approach showing that our representation improves the performance for emotion classification.

Original languageEnglish
Title of host publicationProceedings - 13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages179-186
Number of pages8
ISBN (Electronic)9781538623350
DOIs
Publication statusPublished - 5 Jun 2018
Event13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018 - Xi'an, China
Duration: 15 May 201819 May 2018

Conference

Conference13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018
CountryChina
CityXi'an
Period15/05/1819/05/18

Keywords

  • Facial expressions
  • Representation learning
  • Variational auto encoder

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