DeepGait: A Learning Deep Convolutional Representation for View-Invariant Gait Recognition Using Joint Bayesian
Source abstract
Abstract
Human gait, as a soft biometric, helps to recognize people through their walking. To further improve the recognition performance, we propose a novel video sensor-based gait representation, DeepGait, using deep convolutional features and introduce Joint Bayesian to model view variance. DeepGait is generated by using a pre-trained “very deep” network “D-Net” (VGG-D) without any fine-tuning. For non-view setting, DeepGait outperforms hand-crafted representations (e.g., Gait Energy Image, Frequency-Domain Feature and Gait Flow Image, etc.). Furthermore, for cross-view setting, 256-dimensional DeepGait after PCA significantly outperforms the state-of-the-art methods on the OU-ISR large population (OULP) dataset. The OULP dataset, which includes 4007 subjects, makes our result reliable in a statistically reliable way.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Gait Recognition and Analysis · Human Pose and Action Recognition · Video Surveillance and Tracking Methods
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
Industrial Design Institute, Zhejiang University of Technology, Hangzhou 310023, China
Funding
Department of Science and Technology of Zhejiang Province
References (32)
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