This paper proposes a new age estimation method using hybrid features produced by fusing the global and local features of facial images at decision level. The global facial features are extracted using active appearance models (AAM) which contains both the shape and appearance of a human face. In the local feature extraction phase, the wrinkle features are extracted using a set of Gabor filters, capable of extracting deep and fine wrinkles in different directions and the skin features are extracted using local binary patterns (LBP), capable of extracting the detailed textures of skin. After the feature extraction module, three aging functions are modeled separately with multiple linear regression. Then decision level fusion is performed to combine the results of these aging functions to make a final decision. Experimental results showed that the performance of the proposed method was superior to that of the previous methods when using the FG-NET and PAL aging databases.