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Syna: Emotion Recognition based on Spatio-Temporal Machine Learning
KTH, School of Information and Communication Technology (ICT).
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The analysis of emotions in humans is a field that has been studied for centuries. Through the last decade, multiple approaches towards automatic emotion recognition have been developed to tackle the task of making this analysis autonomous. More specifically, facial expressions in the form of Action Units have been considered until now the most efficient way to recognize emotions. In recent years, applying machine learning for this task has shown outstanding improvements in the accuracy of the solutions. Through this technique, the features can now be automatically learned from the training data, instead of relying on expert domain knowledge and hand-crafted rules. In this thesis, I present Syna and DeepSyna, two models capable of classifying emotional expressions by using both spatial and temporal features. The experimental results demonstrate the effectiveness of Syna in constrained environments, while there is still room for improvement in both constrained and in-the-wild settings. DeepSyna, while addressing this problem, on the other hand suffers from data scarcity and irrelevant transfer learning, which can be solved by future work.

Abstract [sv]

Mänsklig känsloigenkänning har studerats i århundraden. Det senaste årtiondet har mängder av tillvägagångssätt för automatiska processer studerats, för att möjliggöra autonomi; mer specifikt så har ansiktsuttryck i form av Action Units ansetts vara mest effektiva. Maskininlärning har dock nyligen visat att enorma framsteg är möjliga vad gäller bra lösningar på problemen. Så kallade features kan nu automatiskt läras in från träningsdata, även utan expertkunskap och heuristik. Jag presenterar här Syna och DeepSyna, två modeller för ändamålet som använder både spatiala och temporala features. Experiment demonstrerar Synas effektivitet i vissa begränsade omgivningar, medan mycket lämnas att önska vad gäller generella sådana. DeepSyna löser detta men lider samtidigt av databristproblem och onödig så kallad transfer learning, vilket här lämnas till framtida arbete.

Place, publisher, year, edition, pages
2017. , p. 66
Series
TRITA-ICT-EX ; 2017:139
Keyword [en]
Emotion recognition, spatio-temporal machine learning
Keyword [sv]
Känsloigenkänning, spatio-temporal maskininlärning
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-215724OAI: oai:DiVA.org:kth-215724DiVA, id: diva2:1149133
Subject / course
Computer Science
Educational program
Master of Science - Computer Science
Supervisors
Examiners
Available from: 2017-10-13 Created: 2017-10-13 Last updated: 2018-01-13Bibliographically approved

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