The existing local binary pattern (LBP) operators have several disadvantages such as rather long histograms, lower discrimination, and sensitivity to noise. Aiming at these problems, we propose the centralized binary pattern (CBP) operator. CBP operator can significantly reduce the histograms’ dimensionality, offer stronger discrimination, and decrease the white noise’s influence on face images. Moreover, for increasing the recognition accuracy and speed, we use multi-radius CBP histogram as face representation and project it onto locality preserving projection (LPP) space to obtain lower dimensional features. Experiments on FERET and CAS-PEAL databases demonstrate that the proposed method is superior to other modern approaches not only in recognition accuracy but also in recognition speed.