Abstract
We propose a view-constrained latent variable model for multi-view facial expression classification. In this model, we first learn a discriminative manifold shared by multiple views of facial expressions, followed by the expression classification in the shared manifold. For learning, we use the expression data from multiple views, however, the inference is performed using the data from a single view. Our experiments on data of posed and spontaneously displayed facial expressions show that the proposed approach outperforms the state-of-the-art methods for multi-view facial expression classification, and several state-of-the-art methods for multi-view learning.
Original language | English |
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Title of host publication | Advances in Visual Computing |
Subtitle of host publication | 10th International Symposium, ISVC 2014, Las Vegas, NV, USA, December 8-10, 2014, Proceedings |
Editors | George Bebis, Richard Boyle, Bahram Parvin, Darko Koracin |
Place of Publication | Cham |
Publisher | Springer |
Pages | 292-303 |
Number of pages | 12 |
Volume | Part II |
ISBN (Electronic) | 978-3-319-14364-4 |
ISBN (Print) | 978-3-319-14363-7 |
DOIs | |
Publication status | Published - Dec 2014 |
Event | 10th International Symposium on Visual Computing, ISVC 2014 - Las Vegas, United States Duration: 8 Dec 2014 → 10 Dec 2014 Conference number: 10 |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer |
Volume | 8888 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 10th International Symposium on Visual Computing, ISVC 2014 |
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Abbreviated title | ISVC |
Country/Territory | United States |
City | Las Vegas |
Period | 8/12/14 → 10/12/14 |
Keywords
- HMI-HF: Human Factors
- EWI-25810
- METIS-309929
- EC Grant Agreement nr.: FP7/2007-2013
- IR-94677
- EC Grant Agreement nr.: FP7/611153