TY - GEN
T1 - Fast and light manifold CNN based 3D facial expression recognition across pose variations
AU - Chen, Zhixing
AU - Wang, Yunhong
AU - Huang, Di
AU - Chen, Liming
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/10/15
Y1 - 2018/10/15
N2 - This paper proposes a novel approach to 3D Facial Expression Recognition (FER), and it is based on a Fast and Light Manifold CNN model, namely FLM-CNN. Different from current manifold CNNs, FLM-CNN adopts a human vision inspired pooling structure and a multi-scale encoding strategy to enhance geometry representation, which highlights shape characteristics of expressions and runs efficiently. Furthermore, a sampling tree based preprocessing method is presented, and it sharply saves memory when applied to 3D facial surfaces, without much information loss of original data. More importantly, due to the property of manifold CNN features of being rotation-invariant, the proposed method shows a high robustness to pose variations. Extensive experiments are conducted on BU-3DFE, and state-of-the-art results are achieved, indicating its effectiveness.
AB - This paper proposes a novel approach to 3D Facial Expression Recognition (FER), and it is based on a Fast and Light Manifold CNN model, namely FLM-CNN. Different from current manifold CNNs, FLM-CNN adopts a human vision inspired pooling structure and a multi-scale encoding strategy to enhance geometry representation, which highlights shape characteristics of expressions and runs efficiently. Furthermore, a sampling tree based preprocessing method is presented, and it sharply saves memory when applied to 3D facial surfaces, without much information loss of original data. More importantly, due to the property of manifold CNN features of being rotation-invariant, the proposed method shows a high robustness to pose variations. Extensive experiments are conducted on BU-3DFE, and state-of-the-art results are achieved, indicating its effectiveness.
KW - 3D Facial Expression Recognition
KW - Deep Learning
KW - Manifold Convolutional Neural Network
KW - Rotation-Invariance
UR - https://www.scopus.com/pages/publications/85058232531
U2 - 10.1145/3240508.3240568
DO - 10.1145/3240508.3240568
M3 - 会议稿件
AN - SCOPUS:85058232531
T3 - MM 2018 - Proceedings of the 2018 ACM Multimedia Conference
SP - 229
EP - 238
BT - MM 2018 - Proceedings of the 2018 ACM Multimedia Conference
PB - Association for Computing Machinery, Inc
T2 - 26th ACM Multimedia conference, MM 2018
Y2 - 22 October 2018 through 26 October 2018
ER -