Abstract
| Original language | English |
|---|---|
| Title of host publication | 2012 21st International conference on Pattern recognition (ICPR 2012) |
| Editors | Jan-Olof Eklundh, Yuichi Ohta, Steven Tanimoto |
| Place of Publication | Tskuba Japan |
| Publisher | IEEE, Institute of Electrical and Electronics Engineers |
| Pages | 1415-1418 |
| Number of pages | 4 |
| ISBN (Electronic) | 9784990644109 |
| ISBN (Print) | 9781467322164 |
| Publication status | Published - 2012 |
| Externally published | Yes |
| Event | 21st International Conference on Pattern Recognition (ICPR 2012) - Tsukuba, Tsukuba, Japan Duration: 11 Nov 2012 → 15 Nov 2012 |
Conference
| Conference | 21st International Conference on Pattern Recognition (ICPR 2012) |
|---|---|
| Abbreviated title | ICPR 2012 |
| Country | Japan |
| City | Tsukuba |
| Period | 11/11/12 → 15/11/12 |
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Fully automatic face recognition from 3D videos. / Hayat, Munawar; Bennamoun, Mohammed; El-Sallam, Amar.
2012 21st International conference on Pattern recognition (ICPR 2012). ed. / Jan-Olof Eklundh; Yuichi Ohta; Steven Tanimoto. Tskuba Japan : IEEE, Institute of Electrical and Electronics Engineers, 2012. p. 1415-1418.Research output: A Conference proceeding or a Chapter in Book › Conference contribution
TY - GEN
T1 - Fully automatic face recognition from 3D videos
AU - Hayat, Munawar
AU - Bennamoun, Mohammed
AU - El-Sallam, Amar
PY - 2012
Y1 - 2012
N2 - Almost all of the existing research on 3D face recognition is based on static 3D images. 3D videos are believed to provide more information in terms of both the shape as well as the dynamics of an individual's face. This paper presents a system which exploits the spatiotemporal information in 3D videos for the task of face recognition. An algorithm for automatic normalization of raw 3D videos is also given. After the detection of the nose tip, all meshes of the 3D video are cropped and uniformly sampled to form range videos. Spatiotemporal Local Binary Pattern (LBP) descriptors are used for feature extraction from the range videos. For classification, a linear multiclass Support Vector Machine (SVM) is used. The system is trained on videos of a person with different facial expressions and tested on a video with new facial expression. Experimental results on the largest currently available 3D video database, BU 4DFE, show a high recognition rate of 92.68%.
AB - Almost all of the existing research on 3D face recognition is based on static 3D images. 3D videos are believed to provide more information in terms of both the shape as well as the dynamics of an individual's face. This paper presents a system which exploits the spatiotemporal information in 3D videos for the task of face recognition. An algorithm for automatic normalization of raw 3D videos is also given. After the detection of the nose tip, all meshes of the 3D video are cropped and uniformly sampled to form range videos. Spatiotemporal Local Binary Pattern (LBP) descriptors are used for feature extraction from the range videos. For classification, a linear multiclass Support Vector Machine (SVM) is used. The system is trained on videos of a person with different facial expressions and tested on a video with new facial expression. Experimental results on the largest currently available 3D video database, BU 4DFE, show a high recognition rate of 92.68%.
KW - Face-recognition
KW - Pattern-recognition
KW - computer-vision
M3 - Conference contribution
SN - 9781467322164
SP - 1415
EP - 1418
BT - 2012 21st International conference on Pattern recognition (ICPR 2012)
A2 - Eklundh, Jan-Olof
A2 - Ohta, Yuichi
A2 - Tanimoto, Steven
PB - IEEE, Institute of Electrical and Electronics Engineers
CY - Tskuba Japan
ER -