We present a method of facial feature point extraction based on improved Active Shape Model (ASM) and Gabor wavelet.Facial feature points can be located robustly and precisely by using the method proposed in this paper.Firstly

light compensation is used on the detected facial region.Secondly

we locate the eyes and mouth to obtain a good initial shape of feature points.And then the improved ASM is used to get a robust rough shape.We improve local gray-level model of the traditional ASM by adding local skin similarity probability

and form local hybrid model.Finally

we search the precise location of a certain feature point by classifying the Gabor feature of points around the rough location.The Gabor feature can be trained and dimensionally reduced by Linear Discriminant Analysis (LDA)

and classified by Nearest Neighborhood method.At the end of the paper

experimental results demonstrate the robustness and accuracy of this method.