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Publication Type : Journal Article
Publisher : International Journal of Intelligent Systems and Applications(IJISA)
Source : International Journal of Intelligent Systems and Applications(IJISA), Vol.10, No.8, pp.85-98, 2018. (Scopus Indexed). DOI: 10.5815/ijisa.2018.08.08,
Url : http://www.mecs-press.org/ijisa/v10n8.html
Campus : Chennai
School : School of Engineering
Center : Amrita Innovation & Research
Department : Electronics and Communication
Verified : Yes
Year : 2018
Abstract : 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
Cite this Research Publication : Manoj Prabhakaran Kumar, Manoj Kumar Rajagopal, "Detecting Happiness in Human Face using Unsupervised Twin-Support Vector Machines", International Journal of Intelligent Systems and Applications(IJISA), Vol.10, No.8, pp.85-98, 2018. (Scopus Indexed). DOI: 10.5815/ijisa.2018.08.08,