Using Spatial Covariance of Geometric and Shape Based Features for Recognition of Basic and Compound Emotions

Introduction / mportance of Study:Compound emotion recognition has been an emerging area of research for the last decade due to its vast applications in surveillance systems, suspicious person detection, detection of mental disorders, pain detection, automated patient observation in hospitals, and driver monitoring. Objectives:This studyfocuseson emotions,highlightingthe fact that theexisting knowledge lacks adequateresearch on compound emotions. This research work emphasizes compound emotions along with basic emotions.Novelty Statement: The contribution of this paper is three-fold. The study proposes an approach relying on geometric and shape-basedfeatures using SVM and then fusing the obtained geometric and shape-based features for both basic as well as compound emotion recognition.Materials and Method:This studyprovidesacomparison with six state-of-the-art approaches in terms of percentage accuracy and time. Dataset:The experiments are performed on a publicly available compound emotion recognition dataset that containsimages with facial fiducial points and action units. Result and Discussion: The results show thattheproposed approach outperformsthe existing approaches. The best accuracy achieved is 98.57% and 77.33% for basic and compound emotion recognition, respectively. Theproposed approach is compared with existing state-of-the-art deep Neural Network architecture. Thecomparison of the proposed approach has been extended further to various existing classifiers both in terms of percentage accuracy and time. Concluding Remarks:The extensive experiments reveal that theproposed approach using SVM outperforms the state-of-the-art deep Neural network architecture and existing classifiers including Naive Bayes, AdaBoost, Decision Table, NNge,and J48