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.