Real time intention recognition

Automatic detection of emotions has many applications, but there is the capacity for improvement with respect to level of automation, accuracy, and speed. A system for recognizing emotions through facial expressions in live video streams and video sequences is presented in this paper. We have implemented an Active shape Model (ASM) tracker, which tracks 116 facial landmarks via web-cam input. The tracked landmark points are used to extract face expression features. An Support Vector Machine (SVM) based classifier is implemented which gives rise to robust our system by recognizing seven expressions rather than only six expression as in the most of face expression systems. This technique is applied for the automated identification of the psychological state that exhibits a very strong correlation with the detected features.