Abstract
Original language | Undefined |
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Title of host publication | IEEE International Conference on Computer Vision, ICCV 2011 |
Place of Publication | USA |
Publisher | IEEE Computer Society |
Pages | 1495-1502 |
Number of pages | 8 |
ISBN (Print) | 978-1-4577-1101-5 |
DOIs | |
Publication status | Published - Nov 2011 |
Event | IEEE International Conference on Computer Vision 2011 - Fira de Barcelona, Barcelona, Spain Duration: 6 Nov 2011 → 13 Nov 2011 |
Publication series
Name | |
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Publisher | IEEE Computer Society |
ISSN (Print) | 1550-5499 |
Conference
Conference | IEEE International Conference on Computer Vision 2011 |
---|---|
Abbreviated title | ICCV 2011 |
Country | Spain |
City | Barcelona |
Period | 6/11/11 → 13/11/11 |
Keywords
- METIS-285022
- IR-79458
- EWI-21316
- HMI-MI: MULTIMODAL INTERACTIONS
- EC Grant Agreement nr.: ERC/203143
Cite this
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Shape-constrained Gaussian Process Regression for Facial-point-based Head-pose Normalization. / Rudovic, Ognjen; Pantic, Maja.
IEEE International Conference on Computer Vision, ICCV 2011. USA : IEEE Computer Society, 2011. p. 1495-1502.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - Shape-constrained Gaussian Process Regression for Facial-point-based Head-pose Normalization
AU - Rudovic, Ognjen
AU - Pantic, Maja
N1 - eemcs-eprint-21316
PY - 2011/11
Y1 - 2011/11
N2 - Given the facial points extracted from an image of a face in an arbitrary pose, the goal of facial-point-based headpose normalization is to obtain the corresponding facial points in a predefined pose (e.g., frontal). This involves inference of complex and high-dimensional mappings due to the large number of the facial points employed, and due to differences in head-pose and facial expression. Most regression-based approaches for learning such mappings focus on modeling correlations only between the inputs (i.e., the facial points in a non-frontal pose) and the outputs (i.e., the facial points in the frontal pose), but not within the inputs and the outputs of the model. This makes these models prone to errors due to noise and outliers in test data, often resulting in anatomically impossible facial configurations formed by their predictions. To address this, we propose Shape-constrained Gaussian Process (SC-GP) regression for facial-point-based head-pose normalization. Specifically, a deformable face-shape model is used to learn a face-shape prior, which is placed on both the input and the output of GP regression in order to constrain the model predictions to anatomically feasible facial configurations. Our extensive experiments on both synthetic and real image data show that the proposed approach generalizes well across poses and handles successfully noise and outliers in test data. In addition, the proposed model outperforms previously proposed approaches to facial-point-based head-pose normalization.
AB - Given the facial points extracted from an image of a face in an arbitrary pose, the goal of facial-point-based headpose normalization is to obtain the corresponding facial points in a predefined pose (e.g., frontal). This involves inference of complex and high-dimensional mappings due to the large number of the facial points employed, and due to differences in head-pose and facial expression. Most regression-based approaches for learning such mappings focus on modeling correlations only between the inputs (i.e., the facial points in a non-frontal pose) and the outputs (i.e., the facial points in the frontal pose), but not within the inputs and the outputs of the model. This makes these models prone to errors due to noise and outliers in test data, often resulting in anatomically impossible facial configurations formed by their predictions. To address this, we propose Shape-constrained Gaussian Process (SC-GP) regression for facial-point-based head-pose normalization. Specifically, a deformable face-shape model is used to learn a face-shape prior, which is placed on both the input and the output of GP regression in order to constrain the model predictions to anatomically feasible facial configurations. Our extensive experiments on both synthetic and real image data show that the proposed approach generalizes well across poses and handles successfully noise and outliers in test data. In addition, the proposed model outperforms previously proposed approaches to facial-point-based head-pose normalization.
KW - METIS-285022
KW - IR-79458
KW - EWI-21316
KW - HMI-MI: MULTIMODAL INTERACTIONS
KW - EC Grant Agreement nr.: ERC/203143
U2 - 10.1109/ICCV.2011.6126407
DO - 10.1109/ICCV.2011.6126407
M3 - Conference contribution
SN - 978-1-4577-1101-5
SP - 1495
EP - 1502
BT - IEEE International Conference on Computer Vision, ICCV 2011
PB - IEEE Computer Society
CY - USA
ER -