Abstract
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the IEEE International Conference on Computer Vision (ICCV 2015) |
| Place of Publication | USA |
| Publisher | IEEE |
| Pages | 3792-3800 |
| Number of pages | 9 |
| DOIs | |
| Publication status | Published - Dec 2015 |
| Event | IEEE International Conference on Computer Vision 2015 - Convention Center in Santiago, Santiago, Chile Duration: 7 Dec 2015 → 13 Dec 2015 http://pamitc.org/iccv15/ |
Publication series
| Name | |
|---|---|
| Publisher | IEEE |
Workshop
| Workshop | IEEE International Conference on Computer Vision 2015 |
|---|---|
| Abbreviated title | ICCV 2015 |
| Country | Chile |
| City | Santiago |
| Period | 7/12/15 → 13/12/15 |
| Internet address |
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Keywords
- HMI-HF: Human Factors
- EC Grant Agreement nr.: FP7/611153
- IR-99579
- METIS-316050
- EWI-26841
Cite this
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Multi-Conditional Latent Variable Model for Joint Facial Action Unit Detection. / Eleftheriadis, Stefanos; Rudovic, Ognjen; Pantic, Maja.
Proceedings of the IEEE International Conference on Computer Vision (ICCV 2015). USA : IEEE, 2015. p. 3792-3800.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - Multi-Conditional Latent Variable Model for Joint Facial Action Unit Detection
AU - Eleftheriadis, Stefanos
AU - Rudovic, Ognjen
AU - Pantic, Maja
N1 - eemcs-eprint-26841
PY - 2015/12
Y1 - 2015/12
N2 - We propose a novel multi-conditional latent variable model for simultaneous facial feature fusion and detection of facial action units. In our approach we exploit the structure-discovery capabilities of generative models such as Gaussian processes, and the discriminative power of classifiers such as logistic function. This leads to superior performance compared to existing classifiers for the target task that exploit either the discriminative or generative property, but not both. The model learning is performed via an efficient, newly proposed Bayesian learning strategy based on Monte Carlo sampling. Consequently, the learned model is robust to data overfitting, regardless of the number of both input features and jointly estimated facial action units. Extensive qualitative and quantitative experimental evaluations are performed on three publicly available datasets (CK+, Shoulder-pain and DISFA). We show that the proposed model outperforms the state-of-the-art methods for the target task on (i) feature fusion, and (ii) multiple facial action unit detection.
AB - We propose a novel multi-conditional latent variable model for simultaneous facial feature fusion and detection of facial action units. In our approach we exploit the structure-discovery capabilities of generative models such as Gaussian processes, and the discriminative power of classifiers such as logistic function. This leads to superior performance compared to existing classifiers for the target task that exploit either the discriminative or generative property, but not both. The model learning is performed via an efficient, newly proposed Bayesian learning strategy based on Monte Carlo sampling. Consequently, the learned model is robust to data overfitting, regardless of the number of both input features and jointly estimated facial action units. Extensive qualitative and quantitative experimental evaluations are performed on three publicly available datasets (CK+, Shoulder-pain and DISFA). We show that the proposed model outperforms the state-of-the-art methods for the target task on (i) feature fusion, and (ii) multiple facial action unit detection.
KW - HMI-HF: Human Factors
KW - EC Grant Agreement nr.: FP7/611153
KW - IR-99579
KW - METIS-316050
KW - EWI-26841
U2 - 10.1109/ICCV.2015.432
DO - 10.1109/ICCV.2015.432
M3 - Conference contribution
SP - 3792
EP - 3800
BT - Proceedings of the IEEE International Conference on Computer Vision (ICCV 2015)
PB - IEEE
CY - USA
ER -