Pose and illumination variations are two major challenges in face detection. Therefore，a novel face detection method based on differential features is proposed. This method extracts first order and second order differential features from images, which are respectively used to train two face detectors using the Gentle Adaboost algorithm with the Classification And Regression Trees(CART) as weak classifiers. Given a new image, the two face detectors are first separately applied to detect candidate faces in the image, and then their detected face regions are combined to give the final face detection results. Thanks to the illumination invariance of first order derivative features and to the rotation invariance of second order derivative features, the proposed differential features based face detection method can better handle the detection of multi-view faces in complex background. The proposed method has been evaluated on the CMU-MIT and FDDB datasets and the results demonstrate its effectiveness.