Gait Recognition via Disentangled Representation Learning

Zhang, Ziyuan; Tran, Luan; Yin, Xi; Atoum, Yousef; Liu, Xiaoming; Wan, Jian; Wang, Nanxin

doi:10.48550/arxiv.1904.04925

Cited by 2 publications

(3 citation statements)

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“…Feature extraction from images or frames of a video. [5,133] Auto Encoder Works by compressing and decompressing features from the input. [85,122,134] Capsule Improve the semantic organization of the outputs from a CNN.…”

Section: Deep Belief Network Deep Belief Networkmentioning

confidence: 99%

“…A training method that relies on the differentiaton of an original input and a generate counterpart from a model, such as a CNN. [76,103,115,133,140]…”

Section: Generative Adversarial Network Generative Adversarial Networkmentioning

confidence: 99%

“…Zhang et al [133] also employed autoencoders for gait recognition. The authors used the technique to tackle a common issue faced by biometrical recognition literature, i.e., dealing with clothes blocking body members' views, such as arms and legs.…”

Section: Autoencodersmentioning

confidence: 99%

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Gait Recognition Based on Deep Learning: A Survey

et al. 2022

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In general, biometry-based control systems may not rely on individual expected behavior or cooperation to operate appropriately. Instead, such systems should be aware of malicious procedures for unauthorized access attempts. Some works available in the literature suggest addressing the problem through gait recognition approaches. Such methods aim at identifying human beings through intrinsic perceptible features, despite dressed clothes or accessories. Although the issue denotes a relatively long-time challenge, most of the techniques developed to handle the problem present several drawbacks related to feature extraction and low classification rates, among other issues. However, deep learning-based approaches recently emerged as a robust set of tools to deal with virtually any image and computer-vision-related problem, providing paramount results for gait recognition as well. Therefore, this work provides a surveyed compilation of recent works regarding biometric detection through gait recognition with a focus on deep learning approaches, emphasizing their benefits and exposing their weaknesses. Besides, it also presents categorized and characterized descriptions of the datasets, approaches, and architectures employed to tackle associated constraints.

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Section: Deep Belief Network Deep Belief Networkmentioning

confidence: 99%

“…A training method that relies on the differentiaton of an original input and a generate counterpart from a model, such as a CNN. [76,103,115,133,140]…”

Section: Generative Adversarial Network Generative Adversarial Networkmentioning

confidence: 99%