Facial expression recognition using capsule networks = 캡슐 네트워크를 이용한 얼굴 표정 인식

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Facial expression understanding is one of the basic universal constructions of nonverbal inter-human communication. The ability to classify facial expressions is crucial for better machine-human interaction. In this thesis, we study emotion classification problem using Capsule Network architecture, which is known for ability to generalize learned characteristics of various datasets. To the best of our knowledge, this is a first approach to learn emotional variance encoding of human face using deep neural networks. The proposed model has facial keypoint detection unit, which encourages emotion classifier to learn critical facial attributes. Using the proposed method, we were able to disentangle universal human expressions and we showed that the neural network could learn several expression action units without any supervision.
Advisors
Kim, Dae-Shikresearcher김대식researcher
Description
한국과학기술원 :전기및전자공학부,
Publisher
한국과학기술원
Issue Date
2019
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2019.2,[iv, 25 p. :]

Keywords

Facial expression▼aclassification▼acapsule network; 얼굴 표정▼a분류▼a캡슐 네트워크

URI
http://hdl.handle.net/10203/266713
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=843439&flag=dissertation
Appears in Collection
EE-Theses_Master(석사논문)
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