Fusing shape and texture information for facial age estimation
This paper presents a new human age estimation method by using multiple feature fusion via facial image analysis. Motivated by the fact that both shape and texture information of facial images 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), a powerful and well-known tool that is well suitable for relating two sets of measurements, 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.