How Confident Are You in Your Estimate of a Human Age? Uncertainty-aware Gait-based Age Estimation by Label Distribution Learning

Abstract

Gait-based age estimation is one of key techniques for many applications (e.g., finding lost children/aged wanders). It is well known that the age estimation uncertainty is highly dependent on ages (i.e., it is generally small for children while is large for adults/the elderly), and it is important to know the uncertainty for the above-mentioned applications. We therefore propose a method of uncertainty-aware gait-based age estimation by introducing a label distribution learning framework. More specifically, we design a network which takes an appearance-based gait feature as an input and outputs discrete label distributions in the integer age domain. Experiments with the world-largest gait database OULP-Age show that the proposed method can successfully represent the uncertainty of age estimation and also outperforms or is comparable to the state-of-the-art methods.