TY - GEN
T1 - Automatic 3D facial expression recognition using geometric scattering representation
AU - Yang, Xudong
AU - Huang, Di
AU - Wang, Yunhong
AU - Chen, Liming
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/7/17
Y1 - 2015/7/17
N2 - Facial Expression Recognition (FER) is one of the most active topics in the domain of computer vision and pattern recognition, and it has received increasing attention for its wide application potentials as well as attractive scientific challenges. In this paper, we present a novel method to automatic 3D FER based on geometric scattering representation. A set of maps of shape features in terms of multiple order differential quantities, i.e. the Normal Maps (NOM) and the Shape Index Maps (SIM), are first jointly adopted to comprehensively describe geometry attributes of the facial surface. The scattering operator is then introduced to further highlight expression related cues on these maps, thereby constructing geometric scattering representations of 3D faces for classification. The scattering descriptor not only encodes distinct local shape changes of various expressions as by several milestone descriptors, such as SIFT, HOG, etc., but also captures subtle information hidden in high frequencies, which is quite crucial to better distinguish expressions that are easily confused. We evaluate the proposed approach on the BU-3DFE database, and the performance is up to 84.8% and 82.7% with two commonly used protocols respectively which is superior to the state of the art ones.
AB - Facial Expression Recognition (FER) is one of the most active topics in the domain of computer vision and pattern recognition, and it has received increasing attention for its wide application potentials as well as attractive scientific challenges. In this paper, we present a novel method to automatic 3D FER based on geometric scattering representation. A set of maps of shape features in terms of multiple order differential quantities, i.e. the Normal Maps (NOM) and the Shape Index Maps (SIM), are first jointly adopted to comprehensively describe geometry attributes of the facial surface. The scattering operator is then introduced to further highlight expression related cues on these maps, thereby constructing geometric scattering representations of 3D faces for classification. The scattering descriptor not only encodes distinct local shape changes of various expressions as by several milestone descriptors, such as SIFT, HOG, etc., but also captures subtle information hidden in high frequencies, which is quite crucial to better distinguish expressions that are easily confused. We evaluate the proposed approach on the BU-3DFE database, and the performance is up to 84.8% and 82.7% with two commonly used protocols respectively which is superior to the state of the art ones.
UR - https://www.scopus.com/pages/publications/84944929596
U2 - 10.1109/FG.2015.7163090
DO - 10.1109/FG.2015.7163090
M3 - 会议稿件
AN - SCOPUS:84944929596
T3 - 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2015
BT - 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2015
Y2 - 4 May 2015 through 8 May 2015
ER -