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Detecting Happiness in Human Face using Unsupervised Twin-Support Vector Machines
Kumar M.P,
Published in MECS Publisher
Volume: 10
Issue: 8
Pages: 85 - 98
This paper aims to finding happiness in human face with minimal feature vectors. In this system, the face detection and tracking are carried out by Constrained Local Model (CLM). Using CLM grid node, the entire and minimal feature vector displacement is obtained through extracted features. The feature vector displacements are computed in multi-classes of Twin- Support Vector Machines (TWSVM) classifier to evaluate the happiness. In training and testing phases, the following databases are used such as MMI database, Cohn-Kanade (CK), Extended-CK, Mahnob-Laughter and also Real Time data. Also, this paper compares the Supervised Support Vector Machines and Unsupervised Twin Support Vector Machines classifier with cross data-validation. Using the normalization of Min-max and Z-norm technique, the overall accuracy of finding happiness are computed as 86.29% and 83.79% respectively. © 2018 MECS.
About the journal
JournalInternational Journal of Intelligent Systems and Applications
PublisherMECS Publisher
Open Access0