Abstract
| Original language | English |
|---|---|
| Title of host publication | Proceedings of 9th International Symposium on Signal Processing and Its Applications, ISSPA 2007 |
| Place of Publication | Los Alamitos, California, USA |
| Publisher | IEEE, Institute of Electrical and Electronics Engineers |
| Pages | 1-4 |
| DOIs | |
| Publication status | Published - 2007 |
| Event | 3D Shape Representation by Fusing Local and Global Information - Sharjah, United Arab Emirates Duration: 1 Jan 2007 → … |
Conference
| Conference | 3D Shape Representation by Fusing Local and Global Information |
|---|---|
| Period | 1/01/07 → … |
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3D Shape Representation by Fusing Local and Global Information. / Al-Osaimi, F.R.M.; Bennamoun, Mohammed; Mian, Ajmal.
Proceedings of 9th International Symposium on Signal Processing and Its Applications, ISSPA 2007. Los Alamitos, California, USA : IEEE, Institute of Electrical and Electronics Engineers, 2007. p. 1-4.Research output: Chapter in Book/Conference paper › Conference paper
TY - GEN
T1 - 3D Shape Representation by Fusing Local and Global Information
AU - Al-Osaimi, F.R.M.
AU - Bennamoun, Mohammed
AU - Mian, Ajmal
PY - 2007
Y1 - 2007
N2 - We present a unified feature representation of 2.5D pointclouds and apply it to face recognition. The representation integrates local and global geometrical cues in a single compact representation using tensor fields. The global cues provide geometrical coherence for the local cues resulting in better descriptiveness of the unified representation. Multiple rank-0 tensor fields are computed at every point from its local neighborhood and from the global structure of the 2.5D pointcloud. The pointcloud is then represented by multiple rank-0 tensor fields which are invariant to rigid transformations. Each local tensor field is integrated with every global field in a 2D histogram which is indexed by a local field in one dimension and a global field in the other dimension. Finally, PCA coefficients of the 2D histograms are concatenated into a single feature vector. The representation was tested on FRGC V2.0 dataset and achieved 93.78% identification rate and 95.37% verification rate at 0.1% FAR.
AB - We present a unified feature representation of 2.5D pointclouds and apply it to face recognition. The representation integrates local and global geometrical cues in a single compact representation using tensor fields. The global cues provide geometrical coherence for the local cues resulting in better descriptiveness of the unified representation. Multiple rank-0 tensor fields are computed at every point from its local neighborhood and from the global structure of the 2.5D pointcloud. The pointcloud is then represented by multiple rank-0 tensor fields which are invariant to rigid transformations. Each local tensor field is integrated with every global field in a 2D histogram which is indexed by a local field in one dimension and a global field in the other dimension. Finally, PCA coefficients of the 2D histograms are concatenated into a single feature vector. The representation was tested on FRGC V2.0 dataset and achieved 93.78% identification rate and 95.37% verification rate at 0.1% FAR.
U2 - 10.1109/ISSPA.2007.4555442
DO - 10.1109/ISSPA.2007.4555442
M3 - Conference paper
SP - 1
EP - 4
BT - Proceedings of 9th International Symposium on Signal Processing and Its Applications, ISSPA 2007
PB - IEEE, Institute of Electrical and Electronics Engineers
CY - Los Alamitos, California, USA
ER -