Ford Global Technolgoies, LLC

Michigan State University

Luan Tran

Kwaku O Prakah-Asante

Jian Wan

Xiaoming Liu

Xi Yin

Mike Blommer

Ziyuan Zhang

Yousef Atoum

Gait, the walking pattern of individuals, is one of the most important biometrics modalities. Most of the existing gait recognition methods take silhouettes or articulated body models as the gait features. These methods suffer from degraded recognition performance when handling confounding variables, such as clothing, carrying and view angle. To remedy this issue, a novel AutoEncoder framework is presented to explicitly disentangle pose and appearance features from RGB imagery and a long short-term memory integration of pose features over time produces the gait feature.

Systems and methods for gait recognition via disentangled representation learning

Growing community of inventors

Grand Rapids, MI, United States of America

Luan Tran