Gait Recognition Robust to Speed Transition Using Mutual Subspace Method

Person recognition from gait images is not robust to speed changes. To deal with this problem, generally existing methods have focused on training a model to transform gait features from various speeds into a common walking speed, and the model was trained with gait images with a variety of speeds. However in case that a subject walks with a speed which is not trained in the model, the performance gets worse. In this paper we introduce an idea that an image set-based matching approach, which omits walking speed information, has a potential to solve the problem. This is based on the assumption that speed information may not be critical information to gait recognition, since speed variations are universal phenomena. To prove the proposed idea, we apply a mutual subspace method to gait images and show the effectiveness of the proposed idea with the OU-ISIR gait speed transition database.