Abstract:
A lot ofwork has been done in the recent years for automatic recognition offacial features but
the existing work do not measure the intensity offacial emotion. Increasing need for behavioral
biometric systems and human-machine interactions demands
recognition where intensity ofthe emotions is also measured. Therefore, in this study the aim
is to detect various facial emotions (happy, sad, fear, disgust, neutral, surprise and angry) using
machine learning algorithms. In this project, HOG and Gabor filter are used for features
extractions where as SVM and KNN algorithm are used for emotions classification. The
accuracy achieved for SVM is 96.62%. and the accuracy for KNN is 94.62%. The results
validate that the facial emotions can be detected with good accuracy using machine learning
algorithms. Further this study
emotion and intensity of emotion recognition.
more efficient facial ^motion
can be improved by performing real-time behavioral facial
