Beyond Facial Expressions: Learning Human Emotion from Body Gestures

Caifeng Shan · Shaogang Gong · Peter William McOwan

126 citations0/0 files storedBritish Machine Vision Conference (BMVC)

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Emotion and Mood Recognition Hand Gesture Recognition Systems Human Pose and Action Recognition

Abstract

Vision-based human affect analysis is an interesting and challenging problem, impacting important applications in many areas. In this paper, beyond facial expressions, we investigate affective body gesture analysis in video sequences, a relatively understudied problem. Spatial-temporal features are exploited for modeling of body gestures. Moreover, we present to fuse facial expression and body gesture at the feature level using Canonical Correlation Analysis (CCA). By establishing the relationship between the two modalities, CCA derives a semantic “affect ” space. Experimental results demonstrate the effectiveness of our approaches. 1

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References

1992Thin slices of expressive behavior as predictors of interpersonal consequences: A meta-analysis.2003Toward an affect-sensitive multimodal human-computer interaction 2006Bi-modal emotion recognition from expressive face and body gestures 2005Multimodal affect recognition in learning environments 2007Human Computing and Machine Understanding of Human Behavior: A Survey 2005Affective multimodal human-computer interaction 2006A Bimodal Face and Body Gesture Database for Automatic Analysis of Human Nonverbal Affective Behavior

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2015A Survey of Autonomous Human Affect Detection Methods for Social Robots Engaged in Natural HRI 2013Sentic blending: Scalable multimodal fusion for the continuous interpretation of semantics and sentics 2020A Multimodal Emotional Human–Robot Interaction Architecture for Social Robots Engaged in Bidirectional Communication 2015Multi-modal Dimensional Emotion Recognition using Recurrent Neural Networks 2016Sparse Kernel Reduced-Rank Regression for Bimodal Emotion Recognition From Facial Expression and Speech 2014

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