Abstract
The original Active Appearance Model(AAM) uses the mean matrix of gradient matrixes instead of a gradient matrix which should be recomputed with respect to a varying parameter at a fitting phase. By this property, the original AAM can guarantee a fast fitting speed because it avoids computation of a gradient matrix of which a computation complexity is high. However, the fixed gradient matrix is not good choice when the distribution of a training database is nonlinear because the mean can not represent the variation of a training database. To overcome this problem, this paper proposes multi subspaces AAM. First, we divide a training database into multi subspaces along the illumination direction, and build the independent AAM for each subspace. At a fitting phase, we adaptively choose a subspace well fit to a target image. However, the parameter update problem is occurred because a subspace can be changed during a fitting phase. To solve this problem, we propose a linear transform matrix on an eigenspace. In experiments, we apply the proposed method to Yale Face Database B and demonstrate that the method is robust for facial images under various illuminations.
| Original language | English |
|---|---|
| Title of host publication | 2008 8th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2008 |
| DOIs | |
| Publication status | Published - 2008 Dec 1 |
| Event | 2008 8th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2008 - Amsterdam, Netherlands Duration: 2008 Sep 17 → 2008 Sep 19 |
Other
| Other | 2008 8th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2008 |
|---|---|
| Country | Netherlands |
| City | Amsterdam |
| Period | 08/9/17 → 08/9/19 |
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All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Human-Computer Interaction
- Electrical and Electronic Engineering
Cite this
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Multi subspaces active appearance models. / Junyeong, Yang; Hyeran, Byun.
2008 8th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2008. 2008. 4813334.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Multi subspaces active appearance models
AU - Junyeong, Yang
AU - Hyeran, Byun
PY - 2008/12/1
Y1 - 2008/12/1
N2 - The original Active Appearance Model(AAM) uses the mean matrix of gradient matrixes instead of a gradient matrix which should be recomputed with respect to a varying parameter at a fitting phase. By this property, the original AAM can guarantee a fast fitting speed because it avoids computation of a gradient matrix of which a computation complexity is high. However, the fixed gradient matrix is not good choice when the distribution of a training database is nonlinear because the mean can not represent the variation of a training database. To overcome this problem, this paper proposes multi subspaces AAM. First, we divide a training database into multi subspaces along the illumination direction, and build the independent AAM for each subspace. At a fitting phase, we adaptively choose a subspace well fit to a target image. However, the parameter update problem is occurred because a subspace can be changed during a fitting phase. To solve this problem, we propose a linear transform matrix on an eigenspace. In experiments, we apply the proposed method to Yale Face Database B and demonstrate that the method is robust for facial images under various illuminations.
AB - The original Active Appearance Model(AAM) uses the mean matrix of gradient matrixes instead of a gradient matrix which should be recomputed with respect to a varying parameter at a fitting phase. By this property, the original AAM can guarantee a fast fitting speed because it avoids computation of a gradient matrix of which a computation complexity is high. However, the fixed gradient matrix is not good choice when the distribution of a training database is nonlinear because the mean can not represent the variation of a training database. To overcome this problem, this paper proposes multi subspaces AAM. First, we divide a training database into multi subspaces along the illumination direction, and build the independent AAM for each subspace. At a fitting phase, we adaptively choose a subspace well fit to a target image. However, the parameter update problem is occurred because a subspace can be changed during a fitting phase. To solve this problem, we propose a linear transform matrix on an eigenspace. In experiments, we apply the proposed method to Yale Face Database B and demonstrate that the method is robust for facial images under various illuminations.
UR - http://www.scopus.com/inward/record.url?scp=67650652518&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=67650652518&partnerID=8YFLogxK
U2 - 10.1109/AFGR.2008.4813334
DO - 10.1109/AFGR.2008.4813334
M3 - Conference contribution
AN - SCOPUS:67650652518
SN - 9781424421541
BT - 2008 8th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2008
ER -