In unconstrained environments

face recognition results can be seriously affected by inner and outer factors such as expression

attitude

light conditions and background. In this paper

we mainly study the image alignment based AAM (active appearance model) and local matching approach for face recognition that will be able to enhance the robustness to the change of attitude and expression.AAM can rapidly and accurately locate facial feature points

and then warp the picture into a "standard positive" face model.Several models based on Gabor feature have been proposed for face recognition with very good results on available face databases. In this paper

a methodological improvement on Gabor features is proposed and used to align face data by AAM. We select and weight Gabor jets by entropy measure.Then

we bring a threshold to the Borda count classification

eliminating low score jets produced by the voting and consequently

increasing the face recognition rate. Experiments indicate that combination of weighting Gabor jets features with Borda count thresholding can yield the perfect results on face data aligned by AAM.