Integrating Geometric and Textural Features for Facial Emotion Classification Using SVM Frameworks

In this paper, we present a fast facial emotion classification system that relies on the concatenation of geometric and texture-based features. For classification, we propose to leverage the binary classification capabilities of a support vector machine classifier to a hierarchical graph-based architecture that allows multi-class classification. We evaluate our classification results by calculating the emotion-wise classification accuracies and execution time of the hierarchical SVM classifier. A comparison between the overall accuracies of geometric, texture-based, and concatenated features clearly indicates the performance enhancement achieved with concatenated features. Our experiments also demonstrate the effectiveness of our approach for developing efficient and robust real-time facial expression recognition frameworks.