Augmenting Supervised Emotion Recognition with Rule-Based Decision Model

Abstract

The aim of this research is development of rule based decision model for emotion recognition. This research also proposes using the rules for augmenting inter-corporal recognition accuracy in multimodal systems that use supervised learning techniques. The classifiers for such learning based recognition systems are susceptible to over fitting and only perform well on intra-corporal data. To overcome the limitation this research proposes using rule based model as an additional modality. The rules were developed using raw feature data from visual channel, based on human annotator agreement and existing studies that have attributed movement and postures to emotions. The outcome of the rule evaluations was combined during the decision phase of emotion recognition system. The results indicate rule based emotion recognition augment recognition accuracy of learning based systems and also provide better recognition rate across inter corpus emotion test data.

8 pages, 6 figures, 23 tables, IEEE TAC (in review)

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• XML Entity Architecture for Efficient Software Integration
• Multimodal Affect Recognition using Kinect
• Embracing Agile methodology during DevOps Developer Internship Program
• Self-Contained Cross-Cutting Pipeline Software Architecture
• EmoFit: Affect Monitoring System for Sedentary Jobs
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Cited by in corpus (3)

• Edge Based Grid Super-Imposition for Crowd Emotion Recognition
• Dynamic Probabilistic Network Based Human Action Recognition
• Automated Prediction of Temporal Relations