Video and Image based Emotion Recognition Challenges in the Wild: EmotiW 2015

Abhinav DHALL, Ramana ORUGANTI, Roland GOECKE, Jyoti Joshi, Tamas Gedeon

Research output: A Conference proceeding or a Chapter in BookConference contribution

119 Citations (Scopus)

Abstract

The third Emotion Recognition in the Wild (EmotiW) challenge 2015 consists of an audio-video based emotion and static image based facial expression recognition sub-challenges, which mimics real-world conditions. The two sub-challenges are based on the Acted Facial Expression in the Wild (AFEW) 5.0 and the Static Facial Expression in the Wild (SFEW) 2.0 databases, respectively. The paper describes the data, baseline method, challenge protocol and the challenge results. A total of 12 and 17 teams participated in the video based emotion and image based expression sub-challenges, respectively.
Original languageEnglish
Title of host publicationICMI 2015 - Proceedings of the 2015 ACM International Conference on Multimodal Interaction
EditorsPhilip R Cohen
Place of PublicationSeattle, USA
PublisherThe Association for Computing Machinery
Pages423-426
Number of pages4
ISBN (Electronic)9781450339124
ISBN (Print)9781450339124
DOIs
Publication statusPublished - 9 Nov 2015
Event17th ACM International conference on multimodal interaction - Seattle, Seattle, United States
Duration: 9 Nov 201513 Nov 2015

Publication series

NameICMI 2015 - Proceedings of the 2015 ACM International Conference on Multimodal Interaction

Conference

Conference17th ACM International conference on multimodal interaction
Abbreviated titleICMI 2015
CountryUnited States
CitySeattle
Period9/11/1513/11/15

Cite this

DHALL, A., ORUGANTI, R., GOECKE, R., Joshi, J., & Gedeon, T. (2015). Video and Image based Emotion Recognition Challenges in the Wild: EmotiW 2015. In P. R. Cohen (Ed.), ICMI 2015 - Proceedings of the 2015 ACM International Conference on Multimodal Interaction (pp. 423-426). (ICMI 2015 - Proceedings of the 2015 ACM International Conference on Multimodal Interaction). Seattle, USA: The Association for Computing Machinery. https://doi.org/10.1145/2818346.2829994