A Versatile Online System for Person-specific Facial Expression Recognition

In this paper, we introduce an online facial expression recognition (FER) model, which infers the emotional states in real time. This model enables the computer to interact more intelligently with the user. Our proposed mechanism identifies the frontal face along with the region of interest (ROI), extracts discriminating features from suitable facial landmarks, and classifies the facial expressions. Histogram of oriented gradient (HOG) is implemented to extract features and facial landmark positions from active facial regions, which enhances the system performance against all the possible scale and pose variations. The system speed improved with appropriate integration of detection and tracking algorithms. Further, support vector machine (SVM) classifier is used to classify the detected face into neutral or six universal emotions. For achieving the best results with new user faces, the system extracts the neutral features of the user during the time of execution and uses them to train the classifiers. To validate the performance of the proposed algorithm is validated using CK+ and RafD databases.