Feature Fusion In Multimodal Emotion Recognition System For Enhancement Of Human-Machine Interaction. Issue 1 (March 2021)

• Record Type: Journal Article
• Authors: Veni, S; Anand, R; MOHAN, DIVYA; PAUL, ELDHO
• Is Part Of: IOP conference series. Volume 1084:Issue 1(2021)
• Publication Date: 2021-03
• Subjects: Emotion recognition -- Support Vector Machine (SVM) -- prosodic features -- SURF and Gabor Features -- Extraction of energy contour -- Extraction of pitch contour; Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/1084/1/012004
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: Emotion Recognition (ER) systems is very much important for interpersonal relationship. Emotions are developed by some physiological changes. The straightforward of this effort is to discover the competence of language and facemask elements to deliver the feeling exact information for enhancing the Human-Machine interaction. The techniques and systems used in emotion detection may vary depending on the features inspected. Since both these features complement each other, combining them results in higher performance in terms of accuracy of 94.734%. The proposed system was tested on ENTERFACE'05 database and real time video. For Video, Speeded Up Robust Features (SURF) and Gabor features are used.