In this research study, we have develop a technique to analyze and correlate bimodal data sets us...
In this research study, we have develop a technique to analyze and correlate bimodal data sets using energy based fusion model and further recognized the emotional component from these bimodal data sets using Support Vector Machine classifier.
We have endeavored to map the audio and video feature of Bi-Modal input to a common energy scale.
The Energy based Bi-Modal data fusion and emotion recognition model was implemented for the ENTERFACE DATABASE and 3 discrete emotions that are happy, anger and fear. The model shows 93.05% accuracy for subject dependent data fusion and emotion recognition of Happy, Anger and Fear Emotions.