Modeling Understanding Level Based Student Face Classification

This paper describes the student's facial emotion recognition to determine their current state of mind. The facial emotion recognition of the student could be envisioned to sense their attention states through a video camera. Therefore, facial images were analyzed to extract the features using Principal Component Analysis (PCA) algorithm. Then, the eigenvalues component were used as an input to the Minimum Distance Classifier which characterized between two categories of students (understand or unsure/confused). These emotions were promising to be significant in modeling the attention states which will be useful in detecting abnormal attention or focus during the teaching and learning session. The ultimate goal of this research is to develop a teaching monitoring system by modeling the understanding and attention level. Therefore, the low attention level model will alarm a warning signal which indicates the current situation of teaching delivery, quality contents and learning comprehension.