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.

Manoj Kumar R

Electronics

School of Electronics Engineering

Chennai Campus

Kumar M.P

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

International Journal of Intelligent Systems and Applications

In this paper, human facial emotions are detected through normalized minimal feature vectors using semi-supervised Twin Support Vector Machine (TWSVM) learning. In this study, face detection and tracking are carried out using the Constrained Local Model (CLM), which has 66 entire feature vectors. Based on Facial Animation Parameter’s (FAPs) definition, entire feature vectors are those things that visibly affect human emotion. This paper proposes the 13 minimal feature vectors that have high variance among the entire feature vectors are sufficient to identify the six basic emotions. Using the Max &amp; Min and Z-normalization technique, two types of normalized minimal feature vectors are formed. The novelty of this study is methodological in that the normalized data of minimal feature vectors fed as input to the semi-supervised multi-class TWSVM classifier to classify the human emotions is a new contribution. The macro facial expression datasets are used by a standard database and several real-time datasets. 10-fold and hold out cross-validation is applied with the cross-database (combining standard and real-time). In the experimental result, using ‘One vs One’ and ‘One vs All’ multi-class techniques with 3 kernel functions produce a 36 trained model of each emotion and their validation parameters are calculated. The overall accuracy achieved for 10-fold cross-validation is 93.42 ± 3.25% and hold out cross-validation is 92.05 ± 3.79%. The overall performance (Precision, Recall, F1-score, Error rate and Computation Time) of the proposed model was also calculated. The performance of the proposed model and existing methods were compared and results indicate them to be more reliable than existing models. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

Journal	International Journal of Intelligent Systems and Applications
Publisher	MECS Publisher
ISSN	2074-904X
Open Access	0