EmoReact is a newly collected multimodal emotion dataset of children between the ages of four and fourteen years old that contains 1102 videos; the biggest dataset of its kind. This dataset is  annotated for 17 affective states, including six basic emotions (happiness, sadness, surprise, fear, disgust, and anger), neutral, valence and nine complex emotions including curiosity, uncertainty, excitement, attentiveness, exploration, confusion, anxiety, embarrassment and frustration.

To obtain the labels in EmoReact, crowd workers from the online crowd sourcing platform, Amazon’s Mechanical Turk (MTurk)  were recruited. Each video was annotated by three independent workers for seventeen labels. The interface for annotations contained the definitions of each label for consistency and as a test of the rater's vigilance and rational decision making a question about the gender of the child in the video was included. All emotions except valence are annotated on a 1-4 Likert scale where 1 shows the absence of emotion and 4 shows the intense presence of the emotion (with 2 and 3 showing little and moderate expression of the emotion). Valence was annotated on a scale from 1-7; representing strongly negative to strongly positive.

The length of these videos are between 3 seconds to 21 seconds with an average length of about 5 seconds. The emotions have been expressed by 63 different children, 32 female and 31 males, with some diversity in ethnicity. 


Dataset is available here

Related Publications
- EmoReact: A Multimodal Approach and Dataset for Recognizing Emotional Responses in Children

 Behnaz Nojavanasghari, Tadas Baltrusaitis, Charles. E. Hughes, and Louis-philippe Morency,  International Conference on Multimodal Interfaces(ICMI), 2016.[PDF]

- The Future Belongs to the Curious: Towards Automatic Understanding and Recognition of Curiosity in Children

 Behnaz Nojavanasghari, Tadas Baltrusaitis, Charles. E. Hughes, and Louis-philippe Morency, In Proceedings of the    Interspeech Workshop on Child Computer and Interaction(WOCCI), 2016.[PDF]

- Hands-on: Context-driven Hand Gesture Recognition for Automatic Recognition of Curiosity 

Behnaz Nojavanasghari, Charles. E. Hughes, and Louis-philippe Morency,  Designing for Curiosity Workshop at Conference  on Human Factors in Computing Systems. ACM, 2017. [PDF]