Automated gender estimation has numerous applications including video surveillance, human computer-interaction, anonymous customized advertisement and image retrieval. Most commonly, the underlying algorithms analyze the facial appearance for clues of gender. In this work we propose a novel method for gender estimation, which exploits dynamic features gleaned from smiles and we proceed to show that (a) facial dynamics incorporate clues for gender dimorphism, and (b) that while for adult individuals appearance features are more accurate than dynamic features, for subjects under 18 years old facial dynamics can outperform appearance features. In addition , we fuse proposed dynamics-based approach with state-of-the-art appearance based algorithms, predominantly improving appearance-based gender estimation performance. Results show that smile-dynamics include pertinent and complementary to appearance gender information.