NDLI: Capturing Complex Spatio-temporal Relations among Facial Muscles for Facial Expression Recognition

Content Provider	IEEE Xplore Digital Library
Author	Ziheng Wang Shangfei Wang Qiang Ji
Copyright Year	2013
Description	Author affiliation: Sch. of Comput. Sci. & Technol., Univ. of Sci. & Technol. of China, Hefei, China (Ziheng Wang; Qiang Ji)
Abstract	Spatial-temporal relations among facial muscles carry crucial information about facial expressions yet have not been thoroughly exploited. One contributing factor for this is the limited ability of the current dynamic models in capturing complex spatial and temporal relations. Existing dynamic models can only capture simple local temporal relations among sequential events, or lack the ability for incorporating uncertainties. To overcome these limitations and take full advantage of the spatio-temporal information, we propose to model the facial expression as a complex activity that consists of temporally overlapping or sequential primitive facial events. We further propose the Interval Temporal Bayesian Network to capture these complex temporal relations among primitive facial events for facial expression modeling and recognition. Experimental results on benchmark databases demonstrate the feasibility of the proposed approach in recognizing facial expressions based purely on spatio-temporal relations among facial muscles, as well as its advantage over the existing methods.
Starting Page	3422
Ending Page	3429
File Size	1557292
Page Count	8
File Format	PDF
ISBN	9780769549897
ISSN	10636919
DOI	10.1109/CVPR.2013.439
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Face recognition Facial features Facial muscles Bayes methods Mathematical model Muscles Facial Expression Recognition ITBN
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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