Abstract
| Original language | English |
|---|---|
| Title of host publication | Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016 |
| Editors | Yaxin Bi, Supriya Kapoor, Rahul Bhatia |
| Publisher | Springer |
| Pages | 922-931 |
| Number of pages | 10 |
| Volume | 1 |
| ISBN (Electronic) | 9783319569949 |
| ISBN (Print) | 9783319569932 |
| DOIs | |
| Publication status | Published - 2018 |
| Externally published | Yes |
| Event | SAI Intelligent Systems Conference 2016 - CentrEd at ExCel , London, United Kingdom Duration: 21 Sep 2016 → 22 Sep 2016 http://saiconference.com/Conferences/IntelliSys2016 |
Publication series
| Name | Lecture Notes in Networks and Systems |
|---|---|
| Publisher | Sringer |
| Volume | 15 |
| ISSN (Print) | 2367-3370 |
| ISSN (Electronic) | 2367-3389 |
Conference
| Conference | SAI Intelligent Systems Conference 2016 |
|---|---|
| Abbreviated title | IntelliSys 2016 |
| Country | United Kingdom |
| City | London |
| Period | 21/09/16 → 22/09/16 |
| Internet address |
Fingerprint
Cite this
}
Human emotion interpreter : a proposed multi-dimension system for emotion recognition. / ElSayed, Salma.
Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016. ed. / Yaxin Bi; Supriya Kapoor; Rahul Bhatia. Vol. 1 Springer, 2018. p. 922-931 (Lecture Notes in Networks and Systems; Vol. 15).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Human emotion interpreter
T2 - a proposed multi-dimension system for emotion recognition
AU - ElSayed, Salma
PY - 2018
Y1 - 2018
N2 - Human emotion interpretation contributes greatly in Human-Machine Interface (HMI) spanning applications in health care, education, and entertainment. Affective interactions can have the most influence when emotional recognition is available to both human and computers. However, developing robust emotion recognizers is a challenging task in terms of modality, feature selection, and classifier and database design. Most leading research uses facial features, yet verbal communication is also fundamental for sensing affective state especially when visual information is occluded or unavailable. Recent work deploys audiovisual data in bi-modal emotion recognizers. Adding more information e.g. gesture analysis, event/scene understanding, and speaker identification, helps increase recognition accuracy. As classification of human emotions can be considered a multi-modal pattern recognition problem, in this paper, we propose the schematics of a multi-dimension system for automatic human emotion recognition.
AB - Human emotion interpretation contributes greatly in Human-Machine Interface (HMI) spanning applications in health care, education, and entertainment. Affective interactions can have the most influence when emotional recognition is available to both human and computers. However, developing robust emotion recognizers is a challenging task in terms of modality, feature selection, and classifier and database design. Most leading research uses facial features, yet verbal communication is also fundamental for sensing affective state especially when visual information is occluded or unavailable. Recent work deploys audiovisual data in bi-modal emotion recognizers. Adding more information e.g. gesture analysis, event/scene understanding, and speaker identification, helps increase recognition accuracy. As classification of human emotions can be considered a multi-modal pattern recognition problem, in this paper, we propose the schematics of a multi-dimension system for automatic human emotion recognition.
U2 - 10.1007/978-3-319-56994-9
DO - 10.1007/978-3-319-56994-9
M3 - Conference contribution
SN - 9783319569932
VL - 1
T3 - Lecture Notes in Networks and Systems
SP - 922
EP - 931
BT - Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016
A2 - Bi, Yaxin
A2 - Kapoor, Supriya
A2 - Bhatia, Rahul
PB - Springer
ER -