Facial emotion recognition using deep residual networks in real-world environments
- Automatic affect recognition using visual cues is an important task towards a complete interaction between humans and machines. Applications can be found in tutoring systems and human computer interaction. A critical step towards that direction is facial feature extraction. In this paper, we propose a facial feature extractor model trained on an in-the-wild and massively collected video dataset provided by the RealEyes company. The dataset consists of a million labelled frames and 2,616 thousand subjects. As temporal information is important to the emotion recognition domain, we utilise LSTM cells to capture the temporal dynamics in the data. To show the favourable properties of our pre-trained model on modelling facial affect, we use the RECOLA database, and compare with the current state-of-the-art approach. Our model provides the best results in terms of concordance correlation coefficient.
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| Author: | Panagiotis Tzirakis, Dénes Boros, Elnar Hajiyev, Björn W. SchullerORCiDGND |
|---|---|
| URN: | urn:nbn:de:bvb:384-opus4-914822 |
| Frontdoor URL | https://opus.bibliothek.uni-augsburg.de/opus4/91482 |
| Parent Title (English): | arXiv |
| Type: | Preprint |
| Language: | English |
| Date of Publication (online): | 2021/12/21 |
| Year of first Publication: | 2021 |
| Publishing Institution: | Universität Augsburg |
| Release Date: | 2021/12/22 |
| First Page: | arXiv:2111.02717 |
| DOI: | https://doi.org/10.48550/arXiv.2111.02717 |
| Institutes: | Fakultät für Angewandte Informatik |
| Fakultät für Angewandte Informatik / Institut für Informatik | |
| Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Embedded Intelligence for Health Care and Wellbeing | |
| Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
| Licence (German): |  CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand) |
