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.
CITATION STYLE
Datta, S., Sen, D., & Balasubramanian, R. (2017). Integrating geometric and textural features for facial emotion classification using SVM frameworks. In Advances in Intelligent Systems and Computing (Vol. 459 AISC, pp. 619–628). Springer Verlag. https://doi.org/10.1007/978-981-10-2104-6_55
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