Facial Emotion Recognition using Deep Residual Networks in Real-World Environments
This work addresses the problem of improving human-computer interaction through more accurate facial emotion recognition, but it is incremental as it builds on existing deep learning methods with a new dataset.
The paper tackled facial emotion recognition in real-world environments by proposing a deep residual network with LSTM cells trained on a large in-the-wild dataset, achieving state-of-the-art results on the RECOLA database with the best concordance correlation coefficient.
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.