TY - GEN
T1 - Facial expression recognition in daily life by embedded photo reflective sensors on smart eyewear
AU - Masai, Katsutoshi
AU - Sugiura, Yuta
AU - Ogata, Masa
AU - Kunze, Kai
AU - Inami, Masahiko
AU - Sugimoto, Maki
N1 - Funding Information:
This work was supported by the National Natural Science Foundation of China (51306034), Key Research & Development Projects of Jiangsu Province (BE2015677) and the National Basic Research Program of China (2013CB228505).
Publisher Copyright:
© Copyright 2016 ACM.
PY - 2016/3/7
Y1 - 2016/3/7
N2 - This paper presents a novel smart eyewear that uses embedded photo reflective sensors and machine learning to recognize a wearer's facial expressions in daily life. We leverage the skin deformation when wearers change their facial expressions. With small photo reflective sensors, we measure the proximity between the skin surface on a face and the eyewear frame where 17 sensors are integrated. A Support Vector Ma- chine (SVM) algorithm was applied for the sensor information. The sensors can cover various facial muscle movements and can be integrated into everyday glasses. The main contributions of our work are as follows. (1) The eyewear recognizes eight facial expressions (92.8% accuracy for one time use and 78.1% for use on 3 different days). (2) It is designed and implemented considering social acceptability. The device looks like normal eyewear, so users can wear it anytime, anywhere. (3) Initial field trials in daily life were undertaken. Our work is one of the first attempts to recognize and evaluate a variety of facial expressions in the form of an unobtrusive wearable device.
AB - This paper presents a novel smart eyewear that uses embedded photo reflective sensors and machine learning to recognize a wearer's facial expressions in daily life. We leverage the skin deformation when wearers change their facial expressions. With small photo reflective sensors, we measure the proximity between the skin surface on a face and the eyewear frame where 17 sensors are integrated. A Support Vector Ma- chine (SVM) algorithm was applied for the sensor information. The sensors can cover various facial muscle movements and can be integrated into everyday glasses. The main contributions of our work are as follows. (1) The eyewear recognizes eight facial expressions (92.8% accuracy for one time use and 78.1% for use on 3 different days). (2) It is designed and implemented considering social acceptability. The device looks like normal eyewear, so users can wear it anytime, anywhere. (3) Initial field trials in daily life were undertaken. Our work is one of the first attempts to recognize and evaluate a variety of facial expressions in the form of an unobtrusive wearable device.
KW - Eyewear Computing
KW - Facial Expression Recognition
KW - Smart Eye Glasses
KW - Wearable
UR - http://www.scopus.com/inward/record.url?scp=84963757538&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84963757538&partnerID=8YFLogxK
U2 - 10.1145/2856767.2856770
DO - 10.1145/2856767.2856770
M3 - Conference contribution
AN - SCOPUS:84963757538
SN - 9781450341370
T3 - International Conference on Intelligent User Interfaces, Proceedings IUI
SP - 317
EP - 326
BT - Proceedings of the 21st International Conference on Intelligent User Interfaces
PB - Association for Computing Machinery
T2 - 21st International Conference on Intelligent User Interfaces, IUI 2016
Y2 - 7 March 2016 through 10 March 2016
ER -