Gait recognition is an appealing biometric modality which aims to identify individuals based
on the way they walk. Deep learning has reshaped the research landscape in this area …

Gait recognition aims to identify a person at a distance, serving as a promising solution for
long-distance and less-cooperation pedestrian recognition. Recently, significant advances …

Gait benchmarks empower the research community to train and evaluate high-performance
gait recognition systems. Even though growing efforts have been devoted to cross-view …

As a unique biometric feature that can be recognized at a distance, gait has broad
applications in crime prevention, forensic identification and social security. To portray a gait …

We propose in this paper a novel model-based gait recognition method, PoseGait. Gait
recognition is a challenging and attractive task in biometrics. Early approaches to gait …

Gait is a unique biometric feature that can be recognized at a distance; thus, it has broad
applications in crime prevention, forensic identification, and social security. To portray a gait …

In the past few years, deep learning-based models have been very successful in achieving
state-of-the-art results in many tasks in computer vision, speech recognition, and natural …

Variational autoencoders (VAEs), as well as other generative models, have been shown to
be efficient and accurate for capturing the latent structure of vast amounts of complex high …

Compared to other biometrics, gait is difficult to conceal and has the advantage of being
unobtrusive. Inertial sensors, such as accelerometers and gyroscopes, are often used to …

Most existing gait recognition approaches adopt a two-step procedure: a preprocessing step
to extract silhouettes or skeletons followed by recognition. In this paper, we propose an end …