A Multi-component CNN-RNN Approach for Dimensional Emotion Recognition in-the-wild

Abstract

This paper presents our approach to the One-Minute Gradual-Emotion Recognition (OMG-Emotion) Challenge, focusing on dimensional emotion recognition through visual analysis of the provided emotion videos. The approach is based on a Convolutional and Recurrent (CNN-RNN) deep neural architecture we have developed for the relevant large AffWild Emotion Database. We extended and adapted this architecture, by letting a combination of multiple features generated in the CNN component be explored by RNN subnets. Our target has been to obtain best performance on the OMG-Emotion visual validation data set, while learning the respective visual training data set. Extended experimentation has led to best architectures for the estimation of the values of the valence and arousal emotion dimensions over these data sets.

References in corpus (4)

• Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond
• Face Behavior a la carte: Expressions, Affect and Action Units in a Single Network
• Aff-Wild2: Extending the Aff-Wild Database for Affect Recognition
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Cited by in corpus (10)

• Aff-Wild2: Extending the Aff-Wild Database for Affect Recognition
• Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Framework
• MIMAMO Net: Integrating Micro- and Macro-motion for Video Emotion Recognition
• Multitask Emotion Recognition with Incomplete Labels
• Aff-Wild Database and AffWildNet
• Facial Expression Editing with Continuous Emotion Labels
• Emotion Generation and Recognition: A StarGAN Approach
• Interpretable Deep Neural Networks for Facial Expression and Dimensional Emotion Recognition in-the-wild
• Emotion recognition with 4kresolution database
• AI in Pursuit of Happiness, Finding Only Sadness: Multi-Modal Facial Emotion Recognition Challenge