A hybrid strategy for emotion classification

Abstract

Human emotion recognition is an upcoming research field of human computer interaction based on facial gestures and is being used for real-time analysis in classifying cognitive affective states from a facial video data. Since computers have become an integral part of life, many researchers are using emotion recognition and classification of data based on audio and text. But these approaches offer limited accuracy and relevance in emotion classification. Therefore we have introduced and analyzed a hybrid approach which could outperform the existing strategies that uses an innovative approach supported by selection of audio and video data characteristics for classification. The research uses SVM for classifying the data using audio-visual savee database and the results obtained show maximum classification accuracy with respect to audio data about 91.6 could be improved to 99.2% after the application of hybrid strategy.