Biosignal-based user-independent recognition of emotion and personality with importance weighting

For modeling human intelligence, understanding emotional intelligence as well as verbal and mathematical intelligence is an important and challenging issue. In affective and personality computing, it has been reported that not only visual and audio signals but also biosignals are useful for estimating emotions and personality. Biosignals are expected to provide additional and less biased information in implicit assessment, but estimation performance can degrade if there are physiological individual differences. In this paper, considering individual physiological differences as a covariate shift, we aimed to improve the performance results in biosignal-based emotion and personality estimations. For this purpose, we constructed importance-weighted logistic regression (IW-LR) and importance-weighted support vector machine (IW-SVM), which mitigate the accuracy degradation due to physiological individual differences in the training data, and compared them with conventional LR and linear SVM (L-SVM) for estimation performance. As a result, most of the IW models outperform conventional models based on electrocardiogram (ECG) and galvanic skin response (GSR) features in emotion estimation. In the personality estimation, the IW method improves the macroaveraged F1-score for all SVM models. The best performing model (GSR model) outperformed the model with the best previously reported macroaveraged F1-score by 1.9% in personality estimation. These results indicate that importance weighting in machine learning models can reduce the effects of individual physiological differences in peripheral physiological responses and contribute to the proposal of a new model for emotion and personality estimations based on biosignals.