Human Authentication using Gait Recognition | IEEE Machine Learning 2022 Projects
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ABSTRACT
Gait, the walking pattern of individuals, is one of the important biometrics modalities. Most of the existing gait recognition
methods take silhouettes or articulated body models as gait features. These methods suffer from degraded recognition performance
when handling confounding variables, such as clothing, carrying and viewing angle. To remedy this issue, we propose a novel
AutoEncoder framework, GaitNet, to explicitly disentangle appearance, canonical and pose features from RGB imagery. The LSTM
integrates pose features over time as a dynamic gait feature while canonical features are averaged as a static gait feature. Both of them
are utilized as classification features. In addition, we collect a Frontal-View Gait (FVG) dataset to focus on gait recognition from
frontal-view walking, which is a challenging problem since it contains minimal gait cues compared to other views. FVG also includes other
important variations, e.g., walking speed, carrying, and clothing. With extensive experiments on CASIA-B, USF, and FVG datasets, our
method demonstrates superior performance to the SOTA quantitatively, the ability of feature disentanglement qualitatively, and promising
computational efficiency. We further compare our GaitNet with state-of-the-art face recognition to demonstrate the advantages of gait
biometrics identification under certain scenarios

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