TY - GEN
T1 - LPQ based static and dynamic modeling of facial expressions in 3D videos
AU - Zhen, Qingkai
AU - Huang, Di
AU - Wang, Yunhong
AU - Chen, Liming
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - 4D facial expression recognition
KW - LPQ-TOP
UR - https://www.scopus.com/pages/publications/84893066109
U2 - 10.1007/978-3-319-02961-0_15
DO - 10.1007/978-3-319-02961-0_15
M3 - 会议稿件
AN - SCOPUS:84893066109
SN - 9783319029603
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 122
EP - 129
BT - Biometric Recognition - 8th Chinese Conference, CCBR 2013, Proceedings
T2 - 2012 International Conference on Service-Oriented Computing, ICSOC 2012
Y2 - 16 November 2013 through 17 November 2013
ER -