The performance of gait recognition can be adversely affected by many sources of variation
such as view angle, clothing, presence of and type of bag, posture, and occlusion, among …

In this paper, we discuss input/output architectures for convolutional neural network (CNN)-
based cross-view gait recognition. For this purpose, we consider two aspects: verification …

Gait recognition has been proved useful in human identification at a distance. But many
variations such as view, clothing, carrying condition make gait recognition is still challenging …

Gait recognition is a biometric technique used in determining the identity of humans based
on the style and the manner of their walk. Its performance is often degraded by covariate …

This work targets people identification in video based on the way they walk (ie gait). While
classical methods typically derive gait signatures from sequences of binary silhouettes, in …

Gait recognition is an important topic in biometrics. Current works primarily focus on
recognizing a single person's walking gait. However, a person's gait will change when they …

This paper describes a joint intensity metric learning method to improve the robustness of
gait recognition with silhouette-based descriptors such as gait energy images. Because …

The task of identifying people by the way they walk is known asgait recognition'. Although
gait is mainly used for identification, additional tasks as gender recognition or age estimation …

This work targets people identification in video based on the way they walk (ie gait) by using
deep learning architectures. We explore the use of convolutional neural networks (CNN) for …

Gait is a commonly used biometric for human recognition. Its main advantage relies on its
ability to identify people at distances at which other biometrics fail. In this paper, we develop …