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In this paper, a method for automatic facial expression recognition (FER) from video sequences is introduced. The features are extracted from tracking of facial landmarks. Each landmark component is tracked by appropriate method, results in proposing a hybrid technique that is able to achieve high recognition accuracy with limited feature dimensionality. Moreover, our approach aims to increase the system accuracy by increasing the FER recognition accuracy of the most overlapped expressions while achieving low processing time. Thus, the paper introduces also an intelligent Hierarchal Support Vector Machine (HSVM) to reduce the cross-correlation between the confusing expressions. The proposed system was trained and tested using a standard video sequence dataset for six facial expressions, and compared with previous work. Experimental results show an average of 96% recognition accuracy and average processing time of 93 msec.

Intelligent Real-Time Facial Expression Recognition from Video Sequences based on Hybrid Feature Tracking Algorithms