Automatic Emotion Detection Model from Facial Expression

The human face plays a prodigious role for automatic recognition of emotion in the field of identification of human emotion and the interaction between human and computer for some real application like driver state surveillance, personalized learning, health monitoring etc. Most reported facial emotion recognition systems, however, are not fully considered subject-independent dynamic features, so they are not robust enough for real life recognition tasks with subject (human face) variation, head movement and illumination change. In this article we have tried to design an automated framework for emotion detection using facial expression. For human-computer interaction facial expression makes a platform for non-verbal communication. The emotions are effectively changeable happenings that are evoked as a result of impelling force. So in real life application, detection of emotion is very challenging task. Facial expression recognition system requires to overcome the human face having multiple variability such as color, orientation, expression, posture and texture so on. In our framework we have taken frame from live streaming and processed it using Grabor feature extraction and neural network. To detect the emotion facial attributes extraction by principal component analysis is used and a clusterization of different facial expression with respective emotions. Finally to determine facial expressions separately, the processed feature vector is channeled through the already learned pattern classifiers.