Augmenting Supervised Emotion Recognition with Rule-Based Decision Model
This work addresses the issue of poor generalization in multimodal emotion recognition systems for applications like human-computer interaction, though it is incremental as it builds on existing supervised methods.
The researchers tackled the problem of supervised emotion recognition systems overfitting to intra-corporal data by augmenting them with a rule-based decision model, resulting in improved recognition accuracy and better performance across inter-corporal test data.
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.