Volume 5 Number 5 (Sep. 2016)
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IJCCE 2016 Vol.5(5): 350-357 ISSN: 2010-3743
DOI: 10.17706/IJCCE.2016.5.5.350-357

Cognitive-Affective Emotion Classification: Comparing Features Extraction Algorithm Classified by Multi-class Support Vector Machine

Nova Eka Diana, Ahmad Sabiq
Abstract—Emotional quotient (EQ) is one of the main factors determining the outcome of a learning process. A cognitive-affective states that usually appear during a learning process are bored, confuse, and excited/enthusiastic. Emotion state can be detected by identifying human facial expressions. Here, Principal Component analysis (PCA) and Gabor features extract salient information from facial expression database. Each feature space obtained from these methods is then classified using multi-class Support Vector Machine (SVM) with two cross-validation methods, Holdout and 10-fold cross validation. Experiment results show that classification process using Gabor features and 10-fold cross validation of multi-class SVM give the best accuracy rate.

Index Terms—Cognitive-affective emotions, features extraction, multiclass classification, cross-validation.

The authors are with the Faculty of Information Technology, YARSI University, Jakarta, Indonesia.

Cite:Nova Eka Diana, Ahmad Sabiq, "Cognitive-Affective Emotion Classification: Comparing Features Extraction Algorithm Classified by Multi-class Support Vector Machine," International Journal of Computer and Communication Engineering vol. 5, no. 5, pp. 350-357, 2016.

General Information

ISSN: 2010-3743
Frequency: Bimonthly
Editor-in-Chief: Dr. Maode Ma
Abstracting/ Indexing: EI (INSPEC, IET), Google Scholar, Crossref, Engineering & Technology Digital Library, ProQuest, and Electronic Journals Library
E-mail: ijcce@iap.org
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