Abstract: To solve the problem that the available datasets for face age recognition were generally insufficient, and improve the accuracy of face age recognition with the same available dataset, the label distribution learning（LDL） strategy was introduced into the deep learning（DL） framework and named as DL-LDL. Convolutional neural networks were used to automatically extract facial features, and the improved label distribution learning was used to learn the fuzziness and ambiguity between the real age and the adjacent ages for enriching the age information and improving the recognition accuracy. The DL-LDL method was tested on two open datasets of MORPH and FG-NET. The results show that the DL-LDL method can improve the accuracy of age recognition. Compared with the most advanced facial age recognition method at present, the mean absolute errors of MORPH and FG-NET are reduced by 8.2% and 13.8%, respectively.