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Robust Cross-View Gait Identification with Evidence: A Discriminant Gait GAN (DiGGAN) Approach on 10000 People
Gait is an important biometric trait for surveillance and forensic appli...
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The iMaterialist Fashion Attribute Dataset
Large-scale image databases such as ImageNet have significantly advanced...
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GaitSet: Regarding Gait as a Set for Cross-View Gait Recognition
As a unique biometric feature that can be recognized at a distance, gait...
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Dense-View GEIs Set: View Space Covering for Gait Recognition based on Dense-View GAN
Gait recognition has proven to be effective for long-distance human reco...
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A data augmentation methodology for training machine/deep learning gait recognition algorithms
There are several confounding factors that can reduce the accuracy of ga...
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TraND: Transferable Neighborhood Discovery for Unsupervised Cross-domain Gait Recognition
Gait, i.e., the movement pattern of human limbs during locomotion, is a ...
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Insights from BB-MAS – A Large Dataset for Typing, Gait and Swipes of the Same Person on Desktop, Tablet and Phone
Behavioral biometrics are key components in the landscape of research in...
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VersatileGait: A Large-Scale Synthetic Gait Dataset with Fine-GrainedAttributes and Complicated Scenarios
With the motivation of practical gait recognition applications, we propose to automatically create a large-scale synthetic gait dataset (called VersatileGait) by a game engine, which consists of around one million silhouette sequences of 11,000 subjects with fine-grained attributes in various complicated scenarios. Compared with existing real gait datasets with limited samples and simple scenarios, the proposed VersatileGait dataset possesses several nice properties, including huge dataset size, high sample diversity, high-quality annotations, multi-pitch angles, small domain gap with the real one, etc. Furthermore, we investigate the effectiveness of our dataset (e.g., domain transfer after pretraining). Then, we use the fine-grained attributes from VersatileGait to promote gait recognition in both accuracy and speed, and meanwhile justify the gait recognition performance under multi-pitch angle settings. Additionally, we explore a variety of potential applications for research.Extensive experiments demonstrate the value and effective-ness of the proposed VersatileGait in gait recognition along with its associated applications. We will release both VersatileGait and its corresponding data generation toolkit for further studies.
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