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Perceptual facial expression representation
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Facial expressions play an important role in such areas as human communication or medical state evaluation. For machine learning tasks in those areas, it would be beneficial to have a representation of facial expressions which corresponds to human similarity perception.

In this work, the data-driven approach to representation learning of facial expressions is taken. The methodology is built upon Variational Autoencoders and eliminates the appearance-related features from the latent space by using neutral facial expressions as additional inputs. In order to improve the quality of the learned representation, we modify the prior distribution of the latent variable to impose the structure on the latent space that is consistent with human perception of facial expressions.

We conduct the experiments on two datasets and the additionally collected similarity data, show that the human-like topology in the latent representation helps to improve the performance on the stereotypical emotion classification task and demonstrate the benefits of using a probabilistic generative model in exploring the roles of latent dimensions through the generative process.

Abstract [sv]

Ansiktsuttryck spelar en viktig roll i områden som mänsklig kommunikation eller vid utvärdering av medicinska tillstånd. För att tillämpa maskininlärning i dessa områden skulle det vara fördelaktigt att ha en representation av ansiktsuttryck som bevarar människors uppfattning av likhet.

I det här arbetet används ett data-drivet angreppssätt till representationsinlärning av ansiktsuttryck. Metodologin bygger på s. k. Variational Autoencoders och eliminerar utseende-relaterade drag från den latenta rymden genom att använda neutrala ansiktsuttryck som extra input-data. För att förbättra kvaliteten på den inlärda representationen så modifierar vi a priori-distributionen för den latenta variabeln för att ålägga den struktur på den latenta rymden som är överensstämmande med mänsklig perception av ansiktsuttryck.

Vi utför experiment på två dataset och även insamlad likhets-data och visar att den människolika topologin i den latenta representationen hjälper till att förbättra prestandan på en typisk emotionsklassificeringsuppgift samt fördelarna med att använda en probabilistisk generativ modell när man undersöker latenta dimensioners roll i den generativa processen.

Place, publisher, year, edition, pages
2017. , 72 p.
Keyword [en]
representation learning, facial expression, variational autoencoder
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-217307OAI: oai:DiVA.org:kth-217307DiVA: diva2:1155289
Subject / course
Computer Science
Educational program
Master of Science - Machine Learning
Presentation
2017-08-17, 304, Teknikringen 14, Stockholm, 13:58 (English)
Supervisors
Examiners
Available from: 2017-11-09 Created: 2017-11-07 Last updated: 2017-11-09Bibliographically approved

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Mikheeva, Olga
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CiteExportLink to record
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Citation style
  • apa
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