Image processing for student emotion monitoring based on fisherface method

Monitoring academic emotion is an activity to provide information from students' emotions in the class continuously. Some research image processing field had done for face recognition but not been many studies on detect student emotions. This paper aims determine percentage of facial with fisherface and emotional by monitoring changes expressions using landmarks various distances, camera angles, light, attributes used objects. The proposed method uses extraction based presence. Furthermore, identification will be made Euclidean distance finding smallest length training data test data. Emotion detection mathematical calculations drowsiness, focus, focus face. Restful web service as a communication architecture integrate success rate applications obtains 96% percent accuracy recognition. Meanwhile, are emotions, 84 %.