A method for age estimation of facial images is proposed based on the combination of the Gabor wavelets and the histogram sequence of the local binary pattern (LBP). First, the facial images are filtered by the multi-orientation and multi-scale Gabor before Gabor magnitude maps (GMMs) are extracted. Second, the local neighbor pattern on GMMs is extracted by LBP based on local characteristics, which is divided into several sub-blocks from which calculate the histogram sequences. Third, to further reduce the dimension of facial features, PCA is applied to the histogram sequences. Finally, a leave-one-person-out (LOPO) test scheme of the support vector regression (SVR) is used to train and test the face age database. Experimental results show that the method can quickly and effectively estimate the age of human faces.