Abstract

Affective computing has been largely limited in terms of available data resources. The need to collect and annotate diverse in-the-wild datasets has become apparent with the rise of deep learning models, as the default approach to address any computer vision task. Some in-the-wild databases have been proposed. However: i) their size is small, ii) they are not audiovisual, iii) only a small part is manually annotated, iv) they contain a small number of subjects, or v) they are not annotated for all main behavior tasks (valence-arousal estimation, action unit detection and expression classification). To address these, we create the large-scale Aff-Wild2 database, which is the only in-the-wild database containing annotations for all 3 main behavior tasks. It is also the first audiovisual database with annotations for AUs. The Aff-Wild2 is annotated in a per frame basis for the seven basic expressions (i.e., happiness, surprise, anger, disgust, fear, sadness and the neutral state) and a category 'other' (denoting affective states not displayed within the seven basic expression), twelve action units (AUs 1, 2, 4, 6, 7,10,12,15, 23, 24, 25, 26) and valence and arousal. In total Aff-Wild2 consists of 594 videos of around 3M frames. Aff-Wild2 displays a big diversity in terms of subjects' ages, ethnicities and nationalities; it has also great variations and diversities of environments.

How to acquire the data

If you are an academic, (i.e., a person with a permanent position at university, e.g. a professor, but not a Post-doc or a PhD/PG/UG student), you must:
i) fill in this EULA;
ii) use your official academic email (as data cannot be released to personal emails);
iii) send an email to d.kollias@qmul.ac.uk with subject: Aff-Wild2 request by academic;
iv) include in the email the above signed EULA, the reason why you require access to the Aff-Wild2 database, and your official academic website

If you are a Post-doc or a Ph.D. student: your supervisor/advisor should perform the steps mentioned above and include you in the list of additional researchers.

If you are from industry and you want to acquire Aff-Wild2, please send an email from your official industrial email to d.kollias@qmul.ac.uk with subject: Aff-Wild2 request from industry and explain the reason why the database access is needed; also specify if it is needed for research or commercial purposes.

If you are an undergraduate or postgraduate student (UG/PG student), you must:
i) fill in this EULA; you must print and sign the EULA in ink; a wet signature is required
ii) use your official university email (data cannot be released to personal emails);
iii) send an email to d.kollias@qmul.ac.uk with subject: Aff-Wild2 request by student
iv) include in the email the above signed EULA and proof/verification of your current student status (eg student ID card).
To all UG/PG students:
1 ) The database cannot be shared to students for research internships; the supervisor should instead follow the steps outlined in the two above cases (industry or academia).
2) If the email does not contain all the required information or if the email contains incorrect information (e.g. non filled EULA or non signed EULA or not signed in ink EULA or an incorrect EULA), then the database access request will be automatically rejected and there will be no reply to the email-request. 

Due to the high volume of requests, please allow about 14 days for the reply to your request for access.

Important Information

• All the images of the database are obtained from Youtube. We are not responsible for the content nor the meaning of these images.

• You agree not to reproduce, duplicate, copy, sell, trade, resell or exploit for any commercial purposes, any portion of the images and any portion of derived data.

• You agree not to further copy, publish or distribute any portion of annotations of the dataset. Except, for internal use at a single site within the same organization it is allowed to make copies of the dataset.

• We reserve the right to terminate your access to the dataset at any time.