Abstract
| Original language | Undefined |
|---|---|
| Title of host publication | IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR-W 2011) |
| Place of Publication | USA |
| Publisher | IEEE Computer Society |
| Pages | 13-19 |
| Number of pages | 7 |
| ISBN (Print) | 978-1-4577-0529-8 |
| DOIs | |
| Publication status | Published - Jun 2011 |
Publication series
| Name | |
|---|---|
| Publisher | IEEE Computer Society |
Keywords
- METIS-285027
- IR-79433
- Face Recognition
- Databases
- Humans
- EWI-21325
- Principal component analysis
- Speech
- Training
- EC Grant Agreement nr.: ERC/203143
- HMI-MI: MULTIMODAL INTERACTIONS
- Vectors
Cite this
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Facial Behaviometrics: the Case of Facial Deformation in Spontenaous Smile/Laughter. / Zafeiriou, Stefanos; Pantic, Maja.
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR-W 2011). USA : IEEE Computer Society, 2011. p. 13-19.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - Facial Behaviometrics: the Case of Facial Deformation in Spontenaous Smile/Laughter
AU - Zafeiriou, Stefanos
AU - Pantic, Maja
N1 - 10.1109/CVPRW.2011.5981832
PY - 2011/6
Y1 - 2011/6
N2 - In this paper we explore the use of dense facial deformation in spontaneous smile/laughter as a biometric signature. The facial deformation is calculated between a neutral image (as neutral we define the least expressive image of the smile/laughter episode) and the apex of spontaneous smile/laughter (as apex we define the frame of the maximum facial change/deformation) and its complex representation is regarded. Subsequently, supervised and unsupervised complex dimensionality reduction techniques, namely the complex Principal Component Analysis (PCA) and the complex Linear Discriminant Analysis (LDA), are applied at the complex vector fields for feature extraction. We demonstrate the efficacy of facial deformation as a mean for person verification in a database of spontaneous smiles/laughters.
AB - In this paper we explore the use of dense facial deformation in spontaneous smile/laughter as a biometric signature. The facial deformation is calculated between a neutral image (as neutral we define the least expressive image of the smile/laughter episode) and the apex of spontaneous smile/laughter (as apex we define the frame of the maximum facial change/deformation) and its complex representation is regarded. Subsequently, supervised and unsupervised complex dimensionality reduction techniques, namely the complex Principal Component Analysis (PCA) and the complex Linear Discriminant Analysis (LDA), are applied at the complex vector fields for feature extraction. We demonstrate the efficacy of facial deformation as a mean for person verification in a database of spontaneous smiles/laughters.
KW - METIS-285027
KW - IR-79433
KW - Face Recognition
KW - Databases
KW - Humans
KW - EWI-21325
KW - Principal component analysis
KW - Speech
KW - Training
KW - EC Grant Agreement nr.: ERC/203143
KW - HMI-MI: MULTIMODAL INTERACTIONS
KW - Vectors
U2 - 10.1109/CVPRW.2011.5981832
DO - 10.1109/CVPRW.2011.5981832
M3 - Conference contribution
SN - 978-1-4577-0529-8
SP - 13
EP - 19
BT - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR-W 2011)
PB - IEEE Computer Society
CY - USA
ER -