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Perceptual facial expression representation
KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.
KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.ORCID-id: 0000-0002-5750-9655
2018 (engelsk)Inngår i: Proceedings - 13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018, Institute of Electrical and Electronics Engineers (IEEE), 2018, s. 179-186Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

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.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2018. s. 179-186
Emneord [en]
Facial expressions, Representation learning, Variational auto encoder
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-238209DOI: 10.1109/FG.2018.00035ISI: 000454996700025Scopus ID: 2-s2.0-85049386490ISBN: 9781538623350 (tryckt)OAI: oai:DiVA.org:kth-238209DiVA, id: diva2:1265172
Konferanse
13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018, Grand Dynasty Culture HotelXi'an, China, 15 May 2018 through 19 May 2018
Merknad

QC 20181122

Tilgjengelig fra: 2018-11-22 Laget: 2018-11-22 Sist oppdatert: 2019-09-18bibliografisk kontrollert

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