Real and fake emotion detection using enhanced boosted support vector machine algorithm

Differentiating real and fake emotions becomes a new challenge in facial expression recognition and emotion detection. Real and fake emotions should be taken into account when developing an application. Otherwise, a fake emotion can be categorized as real emotion thereby rendering the model as futile. Very limited research has dealt with identifying fake emotions with accuracy as results are in a range of 51–76%. Performance of the available methods in detecting fake emotions is not encouraging. Thus, in this paper, we have proposed Enhanced Boosted Support Vector Machine (EBSVM) algorithm. EBSVM is a novel technique to determine important thresholds required to understand fake emotions. We have created a new dataset named FED comprising both real and fake emotion images of 50 subjects and used them with experiments along with SASE-FE. EBSVM considers the entire data for classification at each iteration using the ensemble classifier. The EBSVM algorithm achieved 98.08% as classification accuracy for different K-fold validations.