Illumination and facial pose conditions have an explicit effect on the performance of face recognition systems, caused by the complicated non-linear variation between feature points and views. In this paper, we present a Kernel similarity based Active Appearance Models(KSAAMs) in which we use a Kernel Method to replace Principal Component Analysis (PCA) which is used for feature extraction in Active Appearance Models. The major advantage of the proposed approach lies in a more efficient search of non-linear varied parameter under complex face illumination and pose variation conditions. As a consequence, images illuminated from different directions, and images with variable poses can easily be synthesized by changing the parameters found by KSAAMs. From the experimental results, the proposed method provides higher accuracy than classical Active Appearance Model for face alignment in a point-to-point error sense. Keywords: AAMs, Kernel methods, dimension reduction