Group-Level Emotion Recognition Using a Unimodal Privacy-Safe Non-Individual Approach

This article presents our unimodal privacy-safe and non-individual proposal for the audio-video group emotion recognition subtask at the Emotion Recognition in the Wild (EmotiW) Challenge 2020 1. This sub challenge aims to classify in the wild videos into three categories: Positive, Neutral and Negative. Recent deep learning models have shown tremendous advances in analyzing interactions between people, predicting human behavior and affective evaluation. Nonetheless, their performance comes from individual-based analysis, which means summing up and averaging scores from individual detections, which inevitably leads to some privacy issues. In this research, we investigated a frugal approach towards a model able to capture the global moods from the whole image without using face or pose detection, or any individual-based feature as input. The proposed methodology mixes state-of-the-art and dedicated synthetic corpora as training sources. With an in-depth exploration of neural network architectures for group-level emotion recognition, we built a VGG-based model achieving 59.13% accuracy on the VGAF test set (eleventh place of the challenge). Given that the analysis is unimodal based only on global features and that the performance is evaluated on a real-world dataset, these results are promising and let us envision extending this model to multimodality for classroom ambiance evaluation, our final target application.

The Role of Emotion in Problem Solving: First Results from Observing Chess

In this paper we present results from recent experiments that suggest that chess players associate emotions to game situations and reactively use these associations to guide search for planning and problem solving. We describe the design of an instrument for capturing and interpreting multimodal signals of humans engaged in solving challenging problems. We review results from a pilot experiment with human experts engaged in solving challenging problems in Chess that revealed an unexpected observation of rapid changes in emotion as players attempt to solve challenging problems. We propose a cognitive model that describes the process by which subjects select chess chunks for use in interpretation of the game situation and describe initial results from a second experiment designed to test this model.