TY - GEN
T1 - Authentic facial expression analysis
AU - Sebe, Nicu
AU - Lew, Michael S.
AU - Cohen, Ira
AU - Sun, Yafei
AU - Gevers, Theo
AU - Huang, Thomas S.
N1 - Copyright:
Copyright 2010 Elsevier B.V., All rights reserved.
PY - 2004
Y1 - 2004
N2 - It is argued that for the computer to be able to interact with humans, it needs to have the communication skills of humans. One of these skills is the ability to understand the emotional state of the person. The most expressive way humans display emotions is through facial expressions. In most facial expression systems and databases, the emotion data was collected by asking the subjects to perform a series of facial expressions. However, these directed or deliberate facial action tasks typically differ in appearance and timing from the authentic facial expressions induced through events in the normal environment of the subject. In this paper, we present our effort in creating an authentic facial expression database based on spontaneous emotions derived from the environment. Furthermore, we test and compare a wide range of classifiers from the machine learning literature that can be used for facial expression classification.
AB - It is argued that for the computer to be able to interact with humans, it needs to have the communication skills of humans. One of these skills is the ability to understand the emotional state of the person. The most expressive way humans display emotions is through facial expressions. In most facial expression systems and databases, the emotion data was collected by asking the subjects to perform a series of facial expressions. However, these directed or deliberate facial action tasks typically differ in appearance and timing from the authentic facial expressions induced through events in the normal environment of the subject. In this paper, we present our effort in creating an authentic facial expression database based on spontaneous emotions derived from the environment. Furthermore, we test and compare a wide range of classifiers from the machine learning literature that can be used for facial expression classification.
UR - http://www.scopus.com/inward/record.url?scp=4544235737&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=4544235737&partnerID=8YFLogxK
U2 - 10.1109/AFGR.2004.1301585
DO - 10.1109/AFGR.2004.1301585
M3 - Conference contribution
AN - SCOPUS:4544235737
SN - 0769521223
SN - 9780769521220
T3 - Proceedings - Sixth IEEE International Conference on Automatic Face and Gesture Recognition
SP - 517
EP - 522
BT - Proceedings - Sixth IEEE International Conference on Automatic Face and Gesture Recognition FGR 2004
T2 - Proceedings - Sixth IEEE International Conference on Automatic Face and Gesture Recognition FGR 2004
Y2 - 17 May 2004 through 19 May 2004
ER -