NDLI: Learning Expressionlets on Spatio-temporal Manifold for Dynamic Facial Expression Recognition

Content Provider	IEEE Xplore Digital Library
Author	Mengyi Liu Shiguang Shan Ruiping Wang Xilin Chen
Copyright Year	2014
Description	Author affiliation: Key Lab. of Intell. Inf. Process., Inst. of Comput. Technol., Beijing, China (Mengyi Liu; Shiguang Shan; Ruiping Wang; Xilin Chen)
Abstract	Facial expression is 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 components: 1) each expression video clip is modeled as a spatio-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 ST modes to statistically unify all the STMs. 3) the local modes on each STM can be instantiated by fitting to UMM, and the corresponding expressionlet is constructed by modeling the variations in each local ST mode. With 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 AFEW. In all cases, our method reports results better than the known state-of-the-art.
Starting Page	1749
Ending Page	1756
File Size	726658
Page Count	8
File Format	PDF
ISBN	9781479951185
ISSN	10636919
DOI	10.1109/CVPR.2014.226
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Databases Manifolds Feature extraction Face recognition Covariance matrices Three-dimensional displays Training
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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