Emotion Recognition from Geometric Facial Patterns

This paper presents emotion recognition model using the system identication principle. A comprehensive data driven model using an extended self-organizing map (SOM) has been developed whose input is a 26 dimensional facial geometric feature vector comprising eye, lip and eyebrow feature points. This paper thus includes an automated generation scheme of this geometric facial feature vector. MMI facial expression database is used to develop non-heuristic model. The emotion recognition accuracy of the proposed scheme has been compared with radial basis function network, and support vector machine based recognition schemes. The experimental result shows that the proposed model is very efficient in recognizing six basic emotions. It also shows that the average recognition rate of the proposed method is better than multi-class support vector machine. (SVM)