Learning Expressionlets via Universal Manifold Model for Dynamic Facial Expression Recognition

doi:10.1109/TIP.2016.2615424

	Learning Expressionlets via Universal Manifold Model for Dynamic Facial Expression Recognition
	Liu, Mengyi 1; Shan, Shiguang 1,2; Wang, Ruiping 1; Chen, Xilin 1
	2016-12-01
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN	1057-7149
卷号	25 期号:12 页码:5920-5932
摘要	Facial expression is a temporally dynamic event which can be decomposed into a set of muscle motions occurring in different facial regions over various time intervals. For dynamic expression recognition, two key issues, temporal alignment and semantics-aware dynamic representation, must be taken into account. In this paper, we attempt to solve both problems via manifold modeling of videos based on a novel mid-level representation, i.e., expressionlet. Specifically, our method contains three key stages: 1) each expression video clip is characterized as a spatial-temporal manifold (STM) formed by dense low-level features; 2) a universal manifold model (UMM) is learned over all low-level features and represented as a set of local modes to statistically unify all the STMs; and 3) the local modes on each STM can be instantiated by fitting to the UMM, and the corresponding expressionlet is constructed by modeling the variations in each local mode. With the above strategy, expression videos are naturally aligned both spatially and temporally. To enhance the discriminative power, the expressionlet-based STM representation is further processed with discriminant embedding. Our method is evaluated on four public expression databases, CK+, MMI, Oulu-CASIA, and FERA. In all cases, our method outperforms the known state of the art by a large margin.
关键词	Facial expression recognition universal manifold model Riemannian manifold discriminant Learning expressionlets
DOI	10.1109/TIP.2016.2615424
收录类别	SCI
语种	英语
资助项目	973 Program[2015CB351802] ; Natural Science Foundation of China[61390511] ; Natural Science Foundation of China[61379083] ; Natural Science Foundation of China[61272321] ; Strategic Priority Research Program of the CAS[XDB02070004] ; Youth Innovation Promotion Association CAS[2015085] ; FiDiPro Program of Tekes
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000388205200016
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：39[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/7955
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Shan, Shiguang
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 2.CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai 200031, Peoples R China
推荐引用方式 GB/T 7714	Liu, Mengyi,Shan, Shiguang,Wang, Ruiping,et al. Learning Expressionlets via Universal Manifold Model for Dynamic Facial Expression Recognition[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2016,25(12):5920-5932.
APA	Liu, Mengyi,Shan, Shiguang,Wang, Ruiping,&Chen, Xilin.(2016).Learning Expressionlets via Universal Manifold Model for Dynamic Facial Expression Recognition.IEEE TRANSACTIONS ON IMAGE PROCESSING,25(12),5920-5932.
MLA	Liu, Mengyi,et al."Learning Expressionlets via Universal Manifold Model for Dynamic Facial Expression Recognition".IEEE TRANSACTIONS ON IMAGE PROCESSING 25.12(2016):5920-5932.