Abstract: The paper developes a novel nonlinear feature extraction method based on wavelet features. Its main idea is that wavelet transform is first employed to preprocess the original training images before the nonlinear mapping and three groups of wavelet features: lowest frequency subimage, horizontal detail and vertical detail, are derived respectively, What follows, Kernel Fisher Discriminant Analysis(KFDA) is performed on three classes of wavelet features. Three final discriminant feature vectors are obtained, from which a feature fusing method is developed. Finally, The experimental results on ORL face databases indicate that the proposed method is more effective than the current KFDA. And, more importantly, its consumed time in feature extraction is only one thirteenth of that of KFDA. Moreover, the experiments also demonstrate that this method is robust in uncontrolled lighting condition.