TY - GEN
T1 - Multi subspaces active appearance models
AU - Junyeong, Yang
AU - Hyeran, Byun
PY - 2008
Y1 - 2008
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
T3 - 2008 8th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2008
BT - 2008 8th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2008
T2 - 2008 8th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2008
Y2 - 17 September 2008 through 19 September 2008
ER -