University Links: Home Page | Site Map
Covenant University Repository

Training Pattern Classifiers with Physiological Cepstral Features to Recognise Human Emotion

Abayomi, Abdultaofeek and Olugbara, O. O. and Adetiba, E. and Heukelman, Delene (2015) Training Pattern Classifiers with Physiological Cepstral Features to Recognise Human Emotion. Springer, 419. pp. 271-280.

[img] PDF
Download (27kB)


The choice of a suitable set of features based on physiological signals to be utilised in enhancing the recognition of human emotion remains a burning issue in affective computing research. In this study, using the MAHNOB-HCI corpus, we extracted cepstral features from the physiological signals of galvanic skin response, electrocardiogram, electroencephalogram, skin temperature and respiration amplitude to train two state of the art pattern classifiers to recognise seven classes of human emotions. The important task of emotion recognition is largely considered a classification problem and on this basis, we carried out experiments in which the extracted physiological cepstral features were transmitted to Gaussian Radial Basis Function (RBF) neural network and Support Vector Machines (SVM) pattern classifiers for human emotion recognition. The RBF neural network pattern classifier gave the recognition accuracy of 99.5 %, while the SVM pattern classifier posted 75.0 % recognition accuracy. These results indicate the suitability of using cepstral features extracted from fused modality physiological signals with the Gaussian RBF neural network pattern classifier for efficient recognition of human emotion in affective computing systems

Item Type: Article
Uncontrolled Keywords: Classifier Emotion Radial Recognition Signal Support Vector
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > T Technology (General)
Divisions: Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User: Mrs Hannah Akinwumi
Date Deposited: 15 Sep 2016 09:32
Last Modified: 15 Sep 2016 09:32

Actions (login required)

View Item View Item