Fusing Body Posture with Facial Expressions for Joint Recognition of Affect in Child-Robot Interaction

Abstract

In this paper we address the problem of multi-cue affect recognition in challenging scenarios such as child-robot interaction. Towards this goal we propose a method for automatic recognition of affect that leverages body expressions alongside facial ones, as opposed to traditional methods that typically focus only on the latter. Our deep-learning based method uses hierarchical multi-label annotations and multi-stage losses, can be trained both jointly and separately, and offers us computational models for both individual modalities, as well as for the whole body emotion. We evaluate our method on a challenging child-robot interaction database of emotional expressions collected by us, as well as on the GEMEP public database of acted emotions by adults, and show that the proposed method achieves significantly better results than facial-only expression baselines.

To be presented in IROS 2019

References in corpus (4)

• Adam: A Method for Stochastic Optimization
• An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
• AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild
• Deep Facial Expression Recognition: A Survey

Cited by in corpus (5)

• Exploiting Emotional Dependencies with Graph Convolutional Networks for Facial Expression Recognition
• Leveraging Semantic Scene Characteristics and Multi-Stream Convolutional Architectures in a Contextual Approach for Video-Based Visual Emotion Recognition in the Wild
• MoEmo Vision Transformer: Integrating Cross-Attention and Movement Vectors in 3D Pose Estimation for HRI Emotion Detection
• Medical Face Masks and Emotion Recognition from the Body: Insights from a Deep Learning Perspective
• Evaluating Temporal Patterns in Applied Infant Affect Recognition