Joint patch and multi-label learning for facial action unit detection

The face is one of the most powerful channel of nonverbal communication. The most commonly used taxonomy to describe facial behaviour is the Facial Action Coding System (FACS). FACS segments the visible effects of facial muscle activation into 30+ action units (AUs). AUs, which may occur alone and i...

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Vydané v:2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Ročník 2015; s. 2207 - 2216
Hlavní autori: Kaili Zhao, Wen-Sheng Chu, De la Torre, Fernando, Cohn, Jeffrey F., Honggang Zhang
Médium: Konferenčný príspevok.. Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.06.2015
Predmet:
Classifiers
Computer vision
Conferences
Convergence
Correlation
Encoding
Face
Facial
Feature extraction
Gold
Joints
Learning
Muscles
Pattern recognition
Segments
ISSN:1063-6919, 1063-6919
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Shrnutí:The face is one of the most powerful channel of nonverbal communication. The most commonly used taxonomy to describe facial behaviour is the Facial Action Coding System (FACS). FACS segments the visible effects of facial muscle activation into 30+ action units (AUs). AUs, which may occur alone and in thousands of combinations, can describe nearly all-possible facial expressions. Most existing methods for automatic AU detection treat the problem using one-vs-all classifiers and fail to exploit dependencies among AU and facial features. We introduce joint-patch and multi-label learning (JPML) to address these issues. JPML leverages group sparsity by selecting a sparse subset of facial patches while learning a multi-label classifier. In four of five comparisons on three diverse datasets, CK+, GFT, and BP4D, JPML produced the highest average F1 scores in comparison with state-of-the art.
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ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2015.7298833