Lie to Me: Shield Your Emotions from Prying Software.

Deep learning approaches for facial Emotion Recognition (ER) obtain high accuracy on basic models, e.g., Ekman's models, in the specific domain of facial emotional expressions.

Enhancing Mouth-Based Emotion Recognition Using Transfer Learning.

This work concludes the first study on mouth-based emotion recognition while adopting a transfer learning approach. Transfer learning results are paramount for mouth-based emotion emotion recognition, because few datasets are available, and most of them include emotional expressions simulated by actors, instead of adopting real-world categorisation. Using transfer learning, we can use fewer training data than training a whole network from scratch, and thus more efficiently fine-tune the network with emotional data and improve the convolutional neural network's performance accuracy in the desired domain.

Emotional sounds of crowds: spectrogram-based analysis using deep learning.

Crowds express emotions as a collective individual, which is evident from the sounds that a crowd produces in particular events, e.g., collective booing, laughing or cheering in sports matches, movies, theaters, concerts, political demonstrations, and riots.