Abstract: Traditional AAM (Active Appearance Models) improved methods on human facial features localization always concentrate on fitting efficiency without any concrete analysis of characteristic of the initial position and model instance, thus the location accuracy and speed are both not ideal. An initial position correction and model instance selection method based on facial features detection and simple 3D pose estimation is proposed. Adaboost algorithm is applied to pre-detection of facial features in the images firstly, then to extract features from the images that could not be detected or have been incompletely detected using facial skin properties in YCbCr color space. Finally，calculate the coordinate of the nose tip and deflection angle of the face according to features region, properly adjust the fitting center position and model instance and introduce linear algebra software ATLAS(Automatically Tuned Linear Algebra Software) into fitting process for matrixes optimization. Simulation experiments on IMM face database show that proposed method has increased the fitting accuracy rate by about 43% and the time consumption is decreased by about 62% comparing with traditional Inverse Compositional AAM Algorithm.