Physiological-based emotion recognition with IRS model

A major challenge in physiology-based emotion recognition is to establish an effective emotion recognizer for multi-users in the user-independent scenario. The recognition result is not satisfied because it ignores the difference in individual response pattern, which can be attributed to IRS (Indivi...

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Bibliographic Details
Published in2014 International Conference on Smart Computing pp. 208 - 215
Main Authors Chao Li, Zhiyong Feng, Chao Xu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2014
Subjects
Data models
Educational institutions
Electrocardiography
Emotion recognition
Feature extraction
Physiology
Predictive models
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DOI10.1109/SMARTCOMP.2014.7043860

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Summary:A major challenge in physiology-based emotion recognition is to establish an effective emotion recognizer for multi-users in the user-independent scenario. The recognition result is not satisfied because it ignores the difference in individual response pattern, which can be attributed to IRS (Individual Response Specificity) and SRS(Stimuli Response Specificity) in psychophysiology. To improve the performance of emotion recognition, this paper proposes a Group-Based IRS model by adaptively matching a suitable recognizer in accordance with user's IRS level. Specifically, the users are put into distinct groups by using cluster analysis techniques, where users within the same group have similar IRS level than other groups. Then physiological data of users from each group is utilized to build the corresponding emotion recognizers. After categorizing a new user into one group according to his IRS level, the new user's emotion state is predicted by the corresponding emotion recognizer. To validate our model, the affective physiological data was collected from 11 subjects in four induced emotions(neutral, sadness, fear and pleasure), three-channel bio-sensors were used to measure users electrocardiogram (ECG), galvanic skin response (GSR) and photo plethysmography (PPG). The results show that the recognition precision in Group-based IRS model is higher than general model.
DOI:10.1109/SMARTCOMP.2014.7043860