EmotiW 2018: Audio-Video, Student Engagement and Group-Level Affect Prediction

Abhinav Dhall; Amanjot Kaur; Roland GOECKE; Tom Gedeon

doi:10.1145/3242969.3264993

EmotiW 2018: Audio-Video, Student Engagement and Group-Level Affect Prediction

Abhinav Dhall
, Amanjot Kaur
, Roland GOECKE
, Tom Gedeon

Information Systems

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

Abstract

This paper details the sixth Emotion Recognition in the Wild (EmotiW) challenge. EmotiW 2018 is a grand challenge in the ACM International Conference on Multimodal Interaction 2018, Colarado, USA. The challenge aims at providing a common platform to researchers working in the affective computing community to benchmark their algorithms on ‘in the wild’ data. This year EmotiW contains three sub-challenges: a) Audio-video based emotion recognition; b) Student engagement prediction; and c) Group-level emotion recognition. The databases, protocols and baselines are discussed in detail.

Original language	English
Title of host publication	Proceedings of the 20th ACM International Conference on Multimodal Interaction (ICMI2018)
Place of Publication	New York, USA
Publisher	Association for Computing Machinery (ACM)
Pages	653-656
Number of pages	4
ISBN (Electronic)	9781450356923
DOIs	https://doi.org/10.1145/3242969.3264993
Publication status	Published - 16 Oct 2018
Event	20th ACM International Conference on Multimodal Interaction 2018 - Boulder, United States Duration: 16 Oct 2018 → 20 Oct 2018 https://icmi.acm.org/2018/

Conference

Conference	20th ACM International Conference on Multimodal Interaction 2018
Abbreviated title	ICMI2018
Country/Territory	United States
City	Boulder
Period	16/10/18 → 20/10/18
Internet address	https://icmi.acm.org/2018/

Access to Document

10.1145/3242969.3264993Licence: Unspecified

Cite this

@inproceedings{7f78aff541cc41f58ed6f0799ad5758e,

title = "EmotiW 2018: Audio-Video, Student Engagement and Group-Level Affect Prediction",

abstract = "This paper details the sixth Emotion Recognition in the Wild (EmotiW) challenge. EmotiW 2018 is a grand challenge in the ACM International Conference on Multimodal Interaction 2018, Colarado, USA. The challenge aims at providing a common platform to researchers working in the affective computing community to benchmark their algorithms on {\textquoteleft}in the wild{\textquoteright} data. This year EmotiW contains three sub-challenges: a) Audio-video based emotion recognition; b) Student engagement prediction; and c) Group-level emotion recognition. The databases, protocols and baselines are discussed in detail.",

keywords = "Grand Challenge, Affect Prediction, Student Engagement, Audio-Video",

author = "Abhinav Dhall and Amanjot Kaur and Roland GOECKE and Tom Gedeon",

year = "2018",

month = oct,

day = "16",

doi = "10.1145/3242969.3264993",

language = "English",

pages = "653--656",

booktitle = "Proceedings of the 20th ACM International Conference on Multimodal Interaction (ICMI2018)",

publisher = "Association for Computing Machinery (ACM)",

address = "United States",

note = "20th ACM International Conference on Multimodal Interaction 2018, ICMI2018 ; Conference date: 16-10-2018 Through 20-10-2018",

url = "https://icmi.acm.org/2018/",

}

Dhall, A, Kaur, A, GOECKE, R & Gedeon, T 2018, EmotiW 2018: Audio-Video, Student Engagement and Group-Level Affect Prediction. in Proceedings of the 20th ACM International Conference on Multimodal Interaction (ICMI2018). Association for Computing Machinery (ACM), New York, USA, pp. 653-656, 20th ACM International Conference on Multimodal Interaction 2018, Boulder, Colorado, United States, 16/10/18. https://doi.org/10.1145/3242969.3264993

EmotiW 2018: Audio-Video, Student Engagement and Group-Level Affect Prediction. / Dhall, Abhinav; Kaur, Amanjot; GOECKE, Roland et al.
Proceedings of the 20th ACM International Conference on Multimodal Interaction (ICMI2018). New York, USA: Association for Computing Machinery (ACM), 2018. p. 653-656.

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

TY - GEN

T1 - EmotiW 2018: Audio-Video, Student Engagement and Group-Level Affect Prediction

AU - Dhall, Abhinav

AU - Kaur, Amanjot

AU - GOECKE, Roland

AU - Gedeon, Tom

PY - 2018/10/16

Y1 - 2018/10/16

N2 - This paper details the sixth Emotion Recognition in the Wild (EmotiW) challenge. EmotiW 2018 is a grand challenge in the ACM International Conference on Multimodal Interaction 2018, Colarado, USA. The challenge aims at providing a common platform to researchers working in the affective computing community to benchmark their algorithms on ‘in the wild’ data. This year EmotiW contains three sub-challenges: a) Audio-video based emotion recognition; b) Student engagement prediction; and c) Group-level emotion recognition. The databases, protocols and baselines are discussed in detail.

AB - This paper details the sixth Emotion Recognition in the Wild (EmotiW) challenge. EmotiW 2018 is a grand challenge in the ACM International Conference on Multimodal Interaction 2018, Colarado, USA. The challenge aims at providing a common platform to researchers working in the affective computing community to benchmark their algorithms on ‘in the wild’ data. This year EmotiW contains three sub-challenges: a) Audio-video based emotion recognition; b) Student engagement prediction; and c) Group-level emotion recognition. The databases, protocols and baselines are discussed in detail.

KW - Grand Challenge

KW - Affect Prediction

KW - Student Engagement

KW - Audio-Video

UR - http://www.mendeley.com/research/emotiw-2018

U2 - 10.1145/3242969.3264993

DO - 10.1145/3242969.3264993

M3 - Conference contribution

SP - 653

EP - 656

BT - Proceedings of the 20th ACM International Conference on Multimodal Interaction (ICMI2018)

PB - Association for Computing Machinery (ACM)

CY - New York, USA

T2 - 20th ACM International Conference on Multimodal Interaction 2018

Y2 - 16 October 2018 through 20 October 2018

ER -

EmotiW 2018: Audio-Video, Student Engagement and Group-Level Affect Prediction

Abstract

Conference

Access to Document

Other files and links

Fingerprint

Cite this