Bimodal Fusion of Emotional Data in an Automotive Environment

Stefan Hoch · Frank Althoff · Gregor McGlaun · Gerhard Rigoll

87 citations0/0 files storedIEEE International Conference on Acoustics Speech and Signal Processing

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Emotion and Mood Recognition Speech Recognition and Synthesis Speech and Audio Processing

Abstract

We present a flexible bimodal approach to person dependent emotion recognition in an automotive environment by adapting an acoustic and a visual monomodal recognizer and combining the individual results on an abstract decision level. The reference database consists of 840 acted audiovisual examples of seven different speakers, expressing the three emotions, positive (joy), negative (anger, irritation) and neutral. Concerning the acoustic module, we calculate the statistics of commonly known low-level features. Facial expressions are evaluated by an SVM classification of Gabor-filtered face regions. At the subsequent integration stage, both monomodal decisions are fused by a weighted linear combination. An evaluation of the recorded examples yields an average recognition rate of 90.7% for the fusion approach. This adds up to a performance gain of nearly 4% compared to the best monomodal recognizer. The system is currently used to improve the usability for automotive infotainment interfaces.

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References

2003Facial expression recognition from video sequences: temporal and static modeling 2003Toward an affect-sensitive multimodal human-computer interaction 2002Recognizing emotion in speech 2003The production and recognition of emotions in speech: features and algorithms 2004Speech emotion recognition combining acoustic features and linguistic information in a hybrid support vector machine-belief network architecture 2003Developing multimodal intelligent affective interfaces for tele-home health care 2004Audio-visual based emotion recognition-a new approach 2005Dynamics of Facial Expression Extracted Automatically from Video

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2017Deep Spatio-Temporal Features for Multimodal Emotion Recognition 2012Audiovisual emotion recognition using ANOVA feature selection method and multi-classifier neural networks 2010Analysis of Real-World Driver's Frustration 2008Audio-visual emotion recognition using an emotion space concept 2007Audio-Visual Spontaneous Emotion Recognition

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