Selective Transfer Machine for Personalized Facial Action Unit Detection

Automatic facial action unit (AFA) detection from video is a long-standing problem in facial expression analysis. Most approaches emphasize choices of features and classifiers. They neglect individual differences in target persons. People vary markedly in facial morphology (e.g., heavy versus delica...

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Vydáno v:2013 IEEE Conference on Computer Vision and Pattern Recognition Ročník 2013; s. 3515 - 3522
Hlavní autoři: Chu, Wen-Sheng, De la Torre, Fernando, Cohn, Jeffery F.
Médium: Konferenční příspěvek Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.01.2013
Témata:
domain adaptation
Face
Facial action unit detection
Gold
Hidden Markov models
Kernel
Support vector machines
SVM
Training
Training data
ISSN:1063-6919, 1063-6919
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Shrnutí:Automatic facial action unit (AFA) detection from video is a long-standing problem in facial expression analysis. Most approaches emphasize choices of features and classifiers. They neglect individual differences in target persons. People vary markedly in facial morphology (e.g., heavy versus delicate brows, smooth versus deeply etched wrinkles) and behavior. Individual differences can dramatically influence how well generic classifiers generalize to previously unseen persons. While a possible solution would be to train person-specific classifiers, that often is neither feasible nor theoretically compelling. The alternative that we propose is to personalize a generic classifier in an unsupervised manner (no additional labels for the test subjects are required). We introduce a transductive learning method, which we refer to Selective Transfer Machine (STM), to personalize a generic classifier by attenuating person-specific biases. STM achieves this effect by simultaneously learning a classifier and re-weighting the training samples that are most relevant to the test subject. To evaluate the effectiveness of STM, we compared STM to generic classifiers and to cross-domain learning methods in three major databases: CK+, GEMEP-FERA and RU-FACS. STM outperformed generic classifiers in all.
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ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2013.451