TY - JOUR
T1 - Ascertain
T2 - Emotion and personality recognition using commercial sensors
AU - Subramanian, Ramanathan
AU - Wache, Julia
AU - Abadi, Mojtaba Khomami
AU - Vieriu, Radu L.
AU - Winkler, Stefan
AU - Sebe, Nicu
N1 - Funding Information:
This study is supported by the Human-Centered Cyber-physical Systems research grant from A*STAR Singapore, the MIUR Cluster Active Ageing at Home project and Sensaura Inc.
Publisher Copyright:
© 2010-2012 IEEE.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2018/4
Y1 - 2018/4
N2 - We present ASCERTAIN-a multimodal databaASe for impliCit pERsonali Ty and Affect recognitIoN using commercial physiological sensors. To our knowledge, ASCERTAIN is the first database to connect personality traits and emotional states via physiological responses. ASCERTAIN contains big-five personality scales and emotional self-ratings of 58 users along with their Electroencephalogram (EEG), Electrocardiogram (ECG), Galvanic Skin Response (GSR) and facial activity data, recorded using off-The-shelf sensors while viewing affective movie clips. We first examine relationships between users' affective ratings and personality scales in the context of prior observations, and then study linear and non-linear physiological correlates of emotion and personality. Our analysis suggests that the emotion-personality relationship is better captured by non-linear rather than linear statistics. We finally attempt binary emotion and personality trait recognition using physiological features. Experimental results cumulatively confirm that personality differences are better revealed while comparing user responses to emotionally homogeneous videos, and above-chance recognition is achieved for both affective and personality dimensions.
AB - We present ASCERTAIN-a multimodal databaASe for impliCit pERsonali Ty and Affect recognitIoN using commercial physiological sensors. To our knowledge, ASCERTAIN is the first database to connect personality traits and emotional states via physiological responses. ASCERTAIN contains big-five personality scales and emotional self-ratings of 58 users along with their Electroencephalogram (EEG), Electrocardiogram (ECG), Galvanic Skin Response (GSR) and facial activity data, recorded using off-The-shelf sensors while viewing affective movie clips. We first examine relationships between users' affective ratings and personality scales in the context of prior observations, and then study linear and non-linear physiological correlates of emotion and personality. Our analysis suggests that the emotion-personality relationship is better captured by non-linear rather than linear statistics. We finally attempt binary emotion and personality trait recognition using physiological features. Experimental results cumulatively confirm that personality differences are better revealed while comparing user responses to emotionally homogeneous videos, and above-chance recognition is achieved for both affective and personality dimensions.
KW - Commercial sensors
KW - Emotion and personality recognition
KW - Multimodal analysis
KW - Physiological signals
UR - http://www.scopus.com/inward/record.url?scp=85047855890&partnerID=8YFLogxK
U2 - 10.1109/TAFFC.2016.2625250
DO - 10.1109/TAFFC.2016.2625250
M3 - Article
AN - SCOPUS:85047855890
SN - 1949-3045
VL - 9
SP - 147
EP - 160
JO - IEEE Transactions on Affective Computing
JF - IEEE Transactions on Affective Computing
IS - 2
ER -