BACKGROUND Age is an essential feature of people, so the study of facial aging should have particular significance. OBJECTIVE The purpose of this study is to improve the performance of age prediction by combining facial landmarks and texture features. METHODS We first measure the distribution of each texture feature. From a geometric point of view, facial feature points will change with age, so it is essential to study facial feature points. We annotate the facial feature points, label the corresponding feature point coordinates, and then use the coordinates of feature points and texture features to predict the age. RESULTS We use the Support Vector Machine regression prediction method to predict the age based on the extracted texture features and landmarks. Compared with facial texture features, the prediction results based on facial landmarks are better. This suggests that the facial morphological features contained in facial landmarks can reflect facial age better than facial texture features. Combined with facial landmarks and texture features, the performance of age prediction can be improved. CONCLUSIONS According to the experimental results, we can conclude that texture features combined with facial landmarks are useful for age prediction.