Estimating human age via facial image analysis is very difficult，due to the fact that the factors of causing variations in the appearance of the human face include not only the aging，but also the lifestyle and life environments etc. Both illumination and position of facial image have side-effect on the age estimation. Existing estimation methods consider the shape or texture of facial image to characterize human aging with the preprocessing of the gray-balance and Procrustes analysis. Motivated by the fact that both LBP and HOG information of facial images are robust to control illumination and rotation and can provide complementary information in characterizing human age，we propose fusing these two sources of information at the feature level by using canonical correlation analysis(CCA) for enhanced facial age estimation. Then，we learn a multiple linear regression function to uncover the relation of the fused features and the ground-truth age values for age prediction. Experimental results are presented to demonstrate the efficacy of the proposed method.