Abstract
Automatic Facial Expression Recognition (FER) is one of the most active topics in the domain of computer vision and pattern recognition. In this paper, we focus on discrete facial expression recognition by using 4D data (i.e. 3D range image sequences), and present a novel method to address such an issue. The Local Phase Quantisation from Three Orthogonal Planes (LPQ-TOP) descriptor is applied to extract both the static and dynamic clues conveyed in facial expressions. On the one hand, it locally captures the shape attributes in each 3D face model (facial range image). On the other hand, it detects the latent temporal information and represents dynamic changes occurred in facial muscle actions. The SVM classifier is finally used to predict the expression type. The experiments are carried out on the BU-4DFE database, and the achieved results demonstrate the effectiveness of the proposed method.
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Zhen, Q., Huang, D., Wang, Y., Chen, L. (2013). LPQ Based Static and Dynamic Modeling of Facial Expressions in 3D Videos. In: Sun, Z., Shan, S., Yang, G., Zhou, J., Wang, Y., Yin, Y. (eds) Biometric Recognition. CCBR 2013. Lecture Notes in Computer Science, vol 8232. Springer, Cham. https://doi.org/10.1007/978-3-319-02961-0_15
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DOI: https://doi.org/10.1007/978-3-319-02961-0_15
Publisher Name: Springer, Cham
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