Video and image based Emotion Recognition challenges in the Wild: EmotiW 2015

Abhinav Dhall, O. V. Ramana Murthy, Roland Goecke, Jyoti Joshi, Tom Gedeon

Research output: Chapter in Book/Report/Conference proceedingConference PaperOtherpeer-review

122 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 publicationProceedings of the 2015 ACM on International Conference on Multimodal Interaction
EditorsDan Bohus, Radu Horaud, Helen Meng
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages423-426
Number of pages4
ISBN (Electronic)9781450339124
DOIs
Publication statusPublished - 2015
Externally publishedYes
EventInternational Conference on Multimodal Interfaces 2015 - Seattle, United States of America
Duration: 9 Nov 201513 Nov 2015
Conference number: 17th
https://icmi.acm.org/2015/

Conference

ConferenceInternational Conference on Multimodal Interfaces 2015
Abbreviated titleICMI 2015
CountryUnited States of America
CitySeattle
Period9/11/1513/11/15
Internet address

Keywords

  • Affect analysis in the wild
  • Audio-video data corpus
  • Facial expression challenge

Cite this

Dhall, A., Ramana Murthy, O. V., Goecke, R., Joshi, J., & Gedeon, T. (2015). Video and image based Emotion Recognition challenges in the Wild: EmotiW 2015. In D. Bohus, R. Horaud, & H. Meng (Eds.), Proceedings of the 2015 ACM on International Conference on Multimodal Interaction (pp. 423-426). Association for Computing Machinery (ACM). https://doi.org/10.1145/2818346.2829994