Gait-Based Age Estimation with Deep Convolutional Neural Network

Abstract

Gait is a unique biometric identifier for its non-invasive and low-cooperative features. Gait-based attribute recognition can play a crucial role in a wide range of applications, such as intelligent surveillance and criminal retrieval. However, due to the lack of data, there are relatively few studies which apply deep convolutional neural networks on gait attribute recognition. In this study, with the new progress in public gait dataset, we proposed a deep convolutional neural network with multi-task learning for gait-based human age estimation. Gait energy images are directly fed into our model for age estimation while gender information is also integrated for improving the performance of age estimation. The experiments on large-scale OULP-Age dataset show that our model outperforms the state-of-the-art.