Experimental Analysis of Emotion Classification Techniques
| Autor: | Florina Ungureanu, Corina Cimpanu, Vasile-Ion Manta, Tiberius Dumitriu |
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| Rok vydání: | 2018 |
| Předmět: |
Visual perception
business.industry Computer science Emotion classification Pattern recognition 02 engineering and technology Emotion assessment Linear discriminant analysis k-nearest neighbors algorithm Support vector machine 03 medical and health sciences ComputingMethodologies_PATTERNRECOGNITION 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing AdaBoost Artificial intelligence business 030217 neurology & neurosurgery |
| Zdroj: | ICCP |
| DOI: | 10.1109/iccp.2018.8516647 |
| Popis: | Existing achievements in the domain of HumanComputer Interaction (HCI) intend to attain a more natural interplay between its involved actors. Automatic and reliable estimations of affective states in particular from physiological signals received much attention lately. From the physiological measures point of view, emotion assessment benefits of pure, unaltered sensations in contrast to facial or vocal measures that can be simulated. In this paper, some physiological measures based classification approaches for assessing the affective state are analyzed in different scenarios. The analysis is performed on the data acquired from Eye-Tracker (ET) sensors, as well as for Heart Rate (HR) and Electro-Dermal Activity (EDA) in visual stimuli based experiments. To this end, a comparison between AdaBoost (AB), K Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) is accomplished examining entropy indices as primary features. |
| Databáze: | OpenAIRE |
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