Age reflects the continuous accumulation of durable effects from the past since birth. Human faces deform with time non-inversely and thus contains their aging information. In addition to its richness with anatomy information, 3D shape of faces could have the advantage of less dependent on pose and independent of illumination, while it hasn't been noticed in literature. Thus, in this work we investigate the age estimation problem from 3D shape of the face. With several descriptions grounding on Riemannian shape analysis of facial curves, we first extracted features from ideas of face Averageness, face Symmetry, its shape variations with Spatial and Gradient descriptors. Then, using the Random Forest-based Regression, experiments are carried out following the Leaving-One-Person-Out (LOPO) protocol on the FRGCv2 dataset. The proposed approach performs with a Mean Absolute Error (MAE) of 3:29 years using a gender-general test protocol. Finally, with the gender-specific experiments, which first separate the 3D scans into Female and Male subsets, then train and test on each gender specific subset in LOPO fashion, we improves the MAE to 3:15 years, which confirms the idea that the aging effect differs with gender.