A model-based gait recognition method with body pose and human prior knowledge

By Rijun Liao, Shiqi Yu, Weizhi An, Yongzhen Huang. Pattern Recognition, 2020

497 citations recorded by the source corpus.

Abstract 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 recognition were mainly appearance-based. The appearance-based features are usually extracted from human body silhouettes, which are easy to compute and have shown to be efficient for recognition tasks. Nevertheless silhouettes shape is not invariant to changes in clothing, and can be subject to drastic variations, due to illumination changes or other external factors. An alternative to silhouette-based features are model-based features. However, they are very challenging to acquire especially for low image resolution. In contrast to previous approaches, our model PoseGait exploits human 3D pose estimated from images by Convolutional Neural Network as the input feature for gait recognition. The 3D pose, defined by the 3D coordinates of joints of the human body, is invariant to view changes and other external factors of variation. We design spatio-temporal features from the 3D pose to improve the recognition rate. Our method is evaluated on two large datasets, CASIA B and CASIA E. The experimental results show that the proposed method can achieve state-of-the-art performance and is robust to view and clothing variations.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/c511b70c99cef2398b4267e3a4e767c95bab371b

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In Biomedical Engineering

In Computer Vision and Pattern Recognition

In Human-Computer Interaction

Citations in the corpus

iLGaCo: Incremental Learning of Gait Covariate Factors (2020, methodology): “Existing gait methods fall into two typical categories: model-based methods [23, 24] and appearance-based methods [3, 6, 21, 26, 42].”
Gaitgraph: Graph Convolutional Network for Skeleton-Based Gait Recognition (2021, methodology): “With robust human pose estimators emerging, other approaches [6, 7] started new model-based methods for gait.”