Ensemble model using hybridization of angles and distances for emotion recognition (HADER). Issue 6 (17th August 2020)

• Record Type: Journal Article
• Authors: Virmani, Deepali; Singh, Gurpreet; Aggarwal, Lokesh; Gupta, Mansi
• Is Part Of: Journal of information & optimization sciences. Volume 41:Issue 6(2020)
• Page Start: 1453
• Page End: 1461
• Publication Date: 2020-08-17
• Subjects: 68T45 -- Machine vision and scene understanding; Emotion Recognition -- Face Detection -- Geometric Features -- Support Vector Machine -- Logistic Regression -- Multilayer Perceptron; Electronic data processing -- Periodicals; Information science -- Periodicals; Mathematical optimization -- Periodicals; 519.6
• DOI: 10.1080/02522667.2020.1802122
• Languages: English
• ISSNs: 0252-2667

Abstract

Abstract: Integration of emotions to develop an integrated emotionally intelligent system is a tricky task. Human face is a complex entity which displays a variety of facial expressions. In this research study, an Ensemble model using hybridization of angles and distances for emotion recognition (HADER) is proposed. Proposed HADER forms a feature set containing a total of 37 angles and distances calculated using fiducial points. Hybridized feature set is submitted to ensemble model to predict the intensity of various emotions. HADER is validated on CK+ and KDEF datasets. Evaluation shows the increased accuracy results of proposed ensemble to be 99.630 and 98.639 on CK+ and KDEF dataset respectively over the existing geometric feature extraction based techniques attaining a highest accuracy of 90% on CK+.