Estimating the age exactly and then producing the younger and older images of the person is important in security systems design. In this paper local binary patterns are used to classify the age from facial images. The local binary patterns (LBP) are fundamental properties of local image texture and the occurrence histogram of these patterns is an effective texture feature for face description. In the study we classify the FERET images according to their ages with 10 years intervals. The faces are divided into small regions from which the LBP histograms are extracted and concatenated into a feature vector to be used as an efficient face descriptor. For every new face presented to the system, spatial LBP histograms are produced and used to classify the image into one of the age classes. In the classification phase, minimum distance, nearest neighbor and k-nearest neighbor classifiers are used. The experimental results have shown that system performance is 80% for age estimation.