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

by   Anastasia Petrova, et al.

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 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.


page 3

page 4

page 5

page 6

page 9


EmotiW 2018: Audio-Video, Student Engagement and Group-Level Affect Prediction

This paper details the sixth Emotion Recognition in the Wild (EmotiW) ch...

Group-level Emotion Recognition using Transfer Learning from Face Identification

In this paper, we describe our algorithmic approach, which was used for ...

An Attention Model for group-level emotion recognition

In this paper we propose a new approach for classifying the global emoti...

Emotion Recognition in the Wild using Deep Neural Networks and Bayesian Classifiers

Group emotion recognition in the wild is a challenging problem, due to t...

Automatic Group Cohesiveness Detection With Multi-modal Features

Group cohesiveness is a compelling and often studied composition in grou...

Group Emotion Recognition Using Machine Learning

Automatic facial emotion recognition is a challenging task that has gain...

Non-Volume Preserving-based Feature Fusion Approach to Group-Level Expression Recognition on Crowd Videos

Group-level emotion recognition (ER) is a growing research area as the d...

Please sign up or login with your details

Forgot password? Click here to reset