GaitPart: Temporal Part-Based Model for Gait Recognition

In this study, we examine the cross-view gait recognition problem. Many existing methods establish global feature representation based on the whole human body shape. However, they ignore some important details of different parts of the human body. In the latest literature, positioning partial regions to learn fine-grained features has been verified to be effective in human identification. But they only consider coarse fine-grained features and ignore the relationship between neighboring regions. Taken the above insights together, we propose a novel model called GaitGP, which learns both important details through fine-grained features and the relationship between neighboring regions through global features. Our GaitGP model mainly consists of the following two aspects. First, we propose a Channel-Attention Feature Extractor (CAFE) to extract the global features, which aggregates the channel-level attention to enhance the spatial information in a novel convolutional component. Second, we present the Global and Partial Feature Combiner (GPFC) to learn different fine-grained features, and combine them with the global features extracted by the CAFE to obtain the relevant information between neighboring regions. Experimental results on the CASIA gait recognition dataset B (CASIA-B), The OU-ISIR gait database, multi-view large population dataset, and The OU-ISIR gait database gait datasets show that our method is superior to the state-of-the-art cross-view gait recognition methods.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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“…Finally, we choose the separate triplet loss [3,4] to train the proposed model.…”

Section: Overall Frameworkmentioning

confidence: 99%

Learning discriminative representation with global and fine‐grained features for cross‐view gait recognition

Xiao

Yang

Xie

et al. 2021

CAAI Trans on Intel Tech

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“…GaitSet [11] extract frame-level feature by CNN and then propose Set Pooling (SP), which is practically an order-less temporal max pooling, to generate the video-level feature map. GaitPart [12] capture temporal information by a short-term motion capture module. These video methods focus on capturing discriminative spatial temporal information, yet do not explicitly consider the issue of gait covariates.…”

Section: Video-based Gait Recognitionmentioning

confidence: 99%

“…These video methods focus on capturing discriminative spatial temporal information, yet do not explicitly consider the issue of gait covariates. Our method is closely related with GaitSet [11] and GaitPart [12], both of which achieve the state-of-the-art performance, and focuses more on cross walking condition gait recognition.…”

Section: Video-based Gait Recognitionmentioning

confidence: 99%

“…Reference [10] uses LSTM to extract feature from pose estimated by OpenPose to get rid of the adverse influence bag-carrying and clothing. Recently proposed videobased methods focus on aggregating frame-level features from silhouettes sequence via temporal pooling such as GaitSet [11] and GaitPart [12], and use triplet loss as supervision signal. These methods achieve the state-of-the-art performance.…”

Section: Introductionmentioning

confidence: 99%

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Gait Recognition via Cross Walking Condition Constraint

Wang¹,

Ling²,

Li³

et al. 2021

Computers, Materials &Amp; Continua

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Gait recognition is a biometric technique that captures human walking pattern using gait silhouettes as input and can be used for long-term recognition. Recently proposed video-based methods achieve high performance. However, gait covariates or walking conditions, i.e., bag carrying and clothing, make the recognition of intra-class gait samples hard. Advanced methods simply use triplet loss for metric learning, which does not take the gait covariates into account. For alleviating the adverse influence of gait covariates, we propose cross walking condition constraint to explicitly consider the gait covariates. Specifically, this approach designs center-based and pair-wise loss functions to decrease discrepancy of intra-class gait samples under different walking conditions and enlarge the distance of inter-class gait samples under the same walking condition. Besides, we also propose a video-based strong baseline model of high performance by applying simple yet effective tricks, which have been validated in other individual recognition fields. With the proposed baseline model and loss functions, our method achieves the state-of-the-art performance.

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“…Therefore, great effort has been put in its advancement [1,2,3]. Typical approaches use a single modality or input data type [4,5,6]. However, this is a big limitation, as nowadays it is easy to find devices (e.g.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Gait Recognition Under Missing Modalities

Delgado-Escaño

Castro

Guil

et al. 2021

2021 IEEE International Conference on Image Processing (ICIP)

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Multimodal systems for gait recognition have gained a lot of attention. However, there is a clear gap in the study of missing modalities, which represents real-life scenarios where sensors fail or data get corrupted. Here, we investigate how to handle missing modalities for gait recognition. We propose a single and flexible framework that uses a variable number of input modalities. For each modality, it consists of a branch and a binary unit indicating whether the modality is available; these are gated and merged together. Finally, it generates a single and compact 'multimodal' gait signature that encodes biometric information of the input. Our framework outperforms the state of the art on TUM-GAID and extensive experiments reveal its effectiveness for handling missing modalities even in the multiview setup of CASIA-B. The code is available online: https://github.com/avagait/gaitmiss.

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GaitPart: Temporal Part-Based Model for Gait Recognition

Cited by 307 publications

References 18 publications

Learning discriminative representation with global and fine‐grained features for cross‐view gait recognition

Learning discriminative representation with global and fine‐grained features for cross‐view gait recognition

Gait Recognition via Cross Walking Condition Constraint

Multimodal Gait Recognition Under Missing Modalities

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