On Learning Disentangled Representations for Gait Recognition

Zhang, Ziyuan; Tran, Luan; Liu, Feng; Liu, Xiaoming

doi:10.1109/tpami.2020.2998790

Cited by 94 publications

(44 citation statements)

References 74 publications

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“…For the OU-ISIR and CASIA-(B) datasets, performance of our method was compared against several benchmark methods including [21,23,[31][32][33]36].…”

Section: Resultsmentioning

confidence: 99%

“…Authors in [32] have used two gait templates: GEI and PEI with a GAN model for recognizing gaits under different angles of view. In the same context, authors in [31] used RGB image sequences as an input of the proposed architecture based on autoencoder networks and LSTM. Using silhouettebased features captured by specified cameras, the authors in [33] proposed a CNN model for predicting the angle and also used it for recognizing the gait.…”

Section: Literature Reviewmentioning

confidence: 99%

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Gait recognition for person re-identification

et al. 2020

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Person re-identification across multiple cameras is an essential task in computer vision applications, particularly tracking the same person in different scenes. Gait recognition, which is the recognition based on the walking style, is mostly used for this purpose due to that human gait has unique characteristics that allow recognizing a person from a distance. However, human recognition via gait technique could be limited with the position of captured images or videos. Hence, this paper proposes a gait recognition approach for person re-identification. The proposed approach starts with estimating the angle of the gait first, and this is then followed with the recognition process, which is performed using convolutional neural networks. Herein, multitask convolutional neural network models and extracted gait energy images (GEIs) are used to estimate the angle and recognize the gait. GEIs are extracted by first detecting the moving objects, using background subtraction techniques. Training and testing phases are applied to the following three recognized datasets: CASIA-(B), OU-ISIR, and OU-MVLP. The proposed method is evaluated for background modeling using the Scene Background Modeling and Initialization (SBI) dataset. The proposed gait recognition method showed an accuracy of more than 98% for almost all datasets. Results of the proposed approach showed higher accuracy compared to obtained results of other methods result for CASIA-(B) and OU-MVLP and form the best results for the OU-ISIR dataset.

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“…For the OU-ISIR and CASIA-(B) datasets, performance of our method was compared against several benchmark methods including [21,23,[31][32][33]36].…”

Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Gait recognition for person re-identification

et al. 2020

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“…Recurrent Neural Networks (RNNs) have been widely applied to temporal or sequence learning problems, achieving competitive performances for different tasks [91], including gait recognition [29], [31], [79], [83], [92], [93], [94]. A layer of RNN is typically composed of several cells, each corresponding to one input element of the sequence, e.g., one frame of a gait video.…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

“…DAE+RNNs. The combination of DAEs and RNNs has recently been proposed for generating sequence-based disentangled features using an LSTM RNN [29], [94]. In this context, a deep encoder-decoder network with novel loss functions was first used to disentangle gait features, namely identity information from appearance and canonical features that mostly contain spurious information for gait recognition.…”

Section: Hybrid Networkmentioning

confidence: 99%

“…In this context, a deep encoder-decoder network with novel loss functions was first used to disentangle gait features, namely identity information from appearance and canonical features that mostly contain spurious information for gait recognition. A multi-layer LSTM was then used to capture temporal dynamics of the gait features to be finally aggregated for the recognition purpose [29], [94].…”

Section: Hybrid Networkmentioning

confidence: 99%

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Deep Gait Recognition: A Survey

Sepas-Moghaddam¹,

Etemad²

2021

Preprint

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Gait recognition is an appealing biometric modality which aims to identify individuals based on the way they walk. Deep learning has reshaped the research landscape in this area since 2015 through the ability to automatically learn discriminative representations. Gait recognition methods based on deep learning now dominate the state-of-the-art in the field and have fostered real-world applications. In this paper, we present a comprehensive overview of breakthroughs and recent developments in gait recognition with deep learning, and cover broad topics including datasets, test protocols, state-of-the-art solutions, challenges, and future research directions. We first review the commonly used gait datasets along with the principles designed for evaluating them. We then propose a novel taxonomy made up of four separate dimensions namely body representation, temporal representation, feature representation, and neural architecture, to help characterize and organize the research landscape and literature in this area. Following our proposed taxonomy, a comprehensive survey of gait recognition methods using deep learning is presented with discussions on their performances, characteristics, advantages, and limitations. We conclude this survey with a discussion on current challenges and mention a number of promising directions for future research in gait recognition.

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