Gait-Based Age Estimation Using a DenseNet

Abstract

Human age is one of important attributes for many potential applications such as digital signage, customer analysis, and gait-based age estimation is promising particularly for surveillance scenarios since it can be available at a distance from a camera. We therefore proposed a method of gait-based age estimation using a deep learning framework to advance the state-of-the-art accuracy. Specifically, we employed DenseNet as one of state-of-the-art network architectures. While the previous method of gait-based age estimation using a deep learning framework was evaluated only with a small-scale gait database, we evaluated the proposed method with OULP-Age, the world’s largest gait database comprising more than 60,000 subjects with age range from 2 to 90 years old. Consequently, we demonstrated that the proposed method outperform existing methods based on both conventional machine learning frameworks for gait-based age estimation and a deep learning framework for gait recognition.