Detecting facial emotions using normalized minimal feature vectors and semi-supervised twin support vector machines classifier

Abstract

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 & 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.