TY - GEN
T1 - Classification of spontaneous and posed smiles by photo-reflective sensors embedded with smart eyewear
AU - Saito, Chisa
AU - Masai, Katsutoshi
AU - Sugimoto, Maki
N1 - Funding Information:
This research was partially supported by JST CREST JP-MJCR14E1
PY - 2020/2/6
Y1 - 2020/2/6
N2 - Smile is one of the representative emotional expressions which is observed frequently in daily life and essential for various non-verbal communications. People make spontaneous smiles and intentional ones. It is important to guess properly whether a person is making a smile spontaneously or intentionally to understand the meaning of smiles. In this study, we propose a smile classification system with smart eyewear that equips photo-reflective sensors and examines whether we can distinguish two types of smiles; spontaneous smiles caused by funny videos and posed smiles evoked by instructions. We extract geometric features: reflection intensity distribution of sensors and temporal features in a time axis. By applying for Support Vector Machine, we observed 94.6% as the mean accuracy among 12 participants when we used both geometric and temporal features with user-dependent training. The result suggested that we can distinguish between spontaneous and posed smile by the sensors embedded with the smart eyewear.
AB - Smile is one of the representative emotional expressions which is observed frequently in daily life and essential for various non-verbal communications. People make spontaneous smiles and intentional ones. It is important to guess properly whether a person is making a smile spontaneously or intentionally to understand the meaning of smiles. In this study, we propose a smile classification system with smart eyewear that equips photo-reflective sensors and examines whether we can distinguish two types of smiles; spontaneous smiles caused by funny videos and posed smiles evoked by instructions. We extract geometric features: reflection intensity distribution of sensors and temporal features in a time axis. By applying for Support Vector Machine, we observed 94.6% as the mean accuracy among 12 participants when we used both geometric and temporal features with user-dependent training. The result suggested that we can distinguish between spontaneous and posed smile by the sensors embedded with the smart eyewear.
KW - Sensor
KW - Smile classification
KW - Wearable device
UR - http://www.scopus.com/inward/record.url?scp=85082437837&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85082437837&partnerID=8YFLogxK
U2 - 10.1145/3374920.3374936
DO - 10.1145/3374920.3374936
M3 - Conference contribution
AN - SCOPUS:85082437837
T3 - TEI 2020 - Proceedings of the 14th International Conference on Tangible, Embedded, and Embodied Interaction
SP - 46
EP - 52
BT - TEI 2020 - Proceedings of the 14th International Conference on Tangible, Embedded, and Embodied Interaction
PB - Association for Computing Machinery, Inc
T2 - 14th International Conference on Tangible, Embedded, and Embodied Interaction, TEI 2020
Y2 - 9 February 2020 through 12 February 2020
ER -