JAIST Repository >
School of Information Science >
Conference Papers >
Conference Papers >

Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/16709

Title: Multimodal Feature Fusion for Human Personality Traits Classification
Authors: Shen, Zhihao
Elibol, Armagan
Chong, Nak Young
Issue Date: 2020-06
Publisher: Korea Robotics Society
Magazine name: Proceedings of the 2020 17th International Conference on Ubiquitous Robots (UR)
Abstract: Similar to human-human social interaction, the process of inferring a user’s personality traits during human-robot interaction plays an important role. Robots need to be endowed with such capability in order to attract user engagement more. In this study, we present our on-going research on obtaining variable-length multimodal features and their fusion to enable social robots to infer human personality traits during face-to-face human-robot interaction. Multimodal nonverbal features, including head motion, face direction, body motion, voice pitch, voice energy, and Mel-frequency Cepstral Coefficient (MFCC), were extracted from videos and audios recorded during the interaction. The different combinations of multimodal features were verified, and their classification performance was compared.
Rights: Zhihao Shen, Armagan Elibol, Nak Young Chong, Multimodal Feature Fusion for Human Personality Traits Classification, Proceedings of the 2020 17th International Conference on Ubiquitous Robots (UR), Kyoto, Japan, June 22-26, Late Breaking Results Paper, 2020. This material is posted here with permission of Korea Robotics Society (KROS).
URI: http://hdl.handle.net/10119/16709
Material Type: author
Appears in Collections:b11-1. 会議発表論文・発表資料 (Conference Papers)

Files in This Item:

File Description SizeFormat
C4_UR20_0190_FI.pdf102KbAdobe PDFView/Open

All items in DSpace are protected by copyright, with all rights reserved.


Contact : Library Information Section, Japan Advanced Institute of Science and Technology