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

• Published in: Multimedia tools and applications Vol. 82; no. 1; pp. 1333 - 1353
• Main Authors: Annadurai, Swaminathan, Arock, Michael, Vadivel, A.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2023; Springer Nature B.V
• Subjects: Algorithms; Classification; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Emotion recognition; Emotions; Face recognition; Iterative methods; Multimedia Information Systems; Special Purpose and Application-Based Systems; Support vector machines; Enhanced boosted SVM; False emotion; Emotion detection; Genuine emotion; Real emotion; True emotion; Fake emotion
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-022-13210-6

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.