Online Model-based Gait Age and Gender Estimation

Allam Shehata, Mohamad Ammar Alsherfawi Aljazaerly, Levin Gäher, Xiang Li, Yasushi Makihara, Yasushi Yagi

Abstract: This paper presents an online human model-based framework for gait-based age and gender estimation from a sequence of monocular frames. More specifically, we fine-tune a human mesh recovery model (i.e., HMR) to estimate the shape and pose parameters of a predefined 3D human model (i.e., SMPL). We then utilize the estimated parameters to predict the age and gender of the walking subject. To make the age and gender estimation task more favorable for real-time applications, we consider estimating the corresponding probability distributions of age and gender, which preserve the prediction uncertainty. Experiments on the world’s largest multi-view gait age and gender estimation dataset showed the superiority of the proposed method compared to the existing appearance-based baseline. We implement online standalone and client-server systems based on the proposed framework to demonstrate the performance of real-time estimation. We further propose a geometric correction step to the input gait sequence for a more generalization capability of the online system.