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Learning Expressionlets via Universal Manifold Model for Dynamic Facial Expression Recognition

文献类型：期刊论文

作者Liu, Mengyi1; Shan, Shiguang1,2; Wang, Ruiping1; Chen, Xilin1
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期2016-12-01
卷号25期号:12页码:5920-5932
关键词Facial expression recognition universal manifold model Riemannian manifold discriminant Learning expressionlets
ISSN号1057-7149
DOI10.1109/TIP.2016.2615424
英文摘要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.
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
语种英语
WOS记录号WOS:000388205200016
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/7954]  
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.

入库方式： OAI收割

来源：计算技术研究所

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