Human gait recognition using localized Grassmann mean representatives with partial least squares regression

Connie, Tee; Goh, Michael Kah Ong; Teoh, Andrew Beng Jin

doi:10.1007/s11042-018-6045-y

Cited by 6 publications

(3 citation statements)

References 46 publications

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“…The procedural intricacies of these steps are systematically outlined in Algorithm 1. To complement the textual explanation, Figure 2 visually illustrates the main steps of the proposed model’s flowchart [ 44 ]. Figure 3 provides an illustrative example of a gait energy image (GEI) for an individual, showcasing the data analyzed.…”

Section: Methodsmentioning

confidence: 99%

A Novel Multi-Scaled Deep Convolutional Structure for Punctilious Human Gait Authentication

Yousef,

Ata,

Rashed

et al. 2024

Biomimetics

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The need for non-interactive human recognition systems to ensure safe isolation between users and biometric equipment has been exposed by the COVID-19 pandemic. This study introduces a novel Multi-Scaled Deep Convolutional Structure for Punctilious Human Gait Authentication (MSDCS-PHGA). The proposed MSDCS-PHGA involves segmenting, preprocessing, and resizing silhouette images into three scales. Gait features are extracted from these multi-scale images using custom convolutional layers and fused to form an integrated feature set. This multi-scaled deep convolutional approach demonstrates its efficacy in gait recognition by significantly enhancing accuracy. The proposed convolutional neural network (CNN) architecture is assessed using three benchmark datasets: CASIA, OU-ISIR, and OU-MVLP. Moreover, the proposed model is evaluated against other pre-trained models using key performance metrics such as precision, accuracy, sensitivity, specificity, and training time. The results indicate that the proposed deep CNN model outperforms existing models focused on human gait. Notably, it achieves an accuracy of approximately 99.9% for both the CASIA and OU-ISIR datasets and 99.8% for the OU-MVLP dataset while maintaining a minimal training time of around 3 min.

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Section: Methodsmentioning

confidence: 99%

A Novel Multi-Scaled Deep Convolutional Structure for Punctilious Human Gait Authentication

Yousef,

Ata,

Rashed

et al. 2024

Biomimetics

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“…Cross-view gait recognition in [24] also employs spatiotemporal HOG characteristics. Numerous strategies have recently been put out to deal with angle variation, many of which exploit silhouette sequences [25,26], suggest additional features [27,28], or train their deep learning models using GEI images [29]. While authors in [27] offered additional features called autocorrelation features where the image at lag time zero is similar to GEI, authors in [24] proposed a gait identification approach called localized Grassmann mean representatives with partial least squares regression (LoGPLS).…”

Section: Literature Reviewmentioning

confidence: 99%

Gait Recognition using Deep Residual Networks

Talal¹,

Oraibi

Wali

2023

Preprint

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In biometric authentication, the subject of distinguishing people by their gait is considered very important. However, it suffers from several challenges, including changing the angle of walking, wearing a coat, and wearing high shoes. The development of artificial intelligence, especially the subject of deep learning, has made a breakthrough in this field. In this research, a new technique was proposed that depends on the use of the conditional generative adversarial network in addition to the Resent networks in order to generate images. The new method relies on the side view angle because it generates various body characteristics. The framework can be divided into three parts: the first part is the process of extracting silhouettes, calculating the gait cycle, and then calculating gait energy images. The second part is generating images through conditional generative adversarial networks and discriminator models. The third part is the process of classifying images using recent networks. Experiments are performed on a public gait recognition dataset called the CASIA database to evaluate our framework. Results demonstrate that the proposed four-stage framework works better than cutting-edge methods, particularly in carrying-bag and wearing-coat sequences.

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“…Recently, many methods have been proposed for handling the angle variation exploiting the silhouette sequences [26,28] or proposing some other features [22,32], or using GEI images for training their deep learning models [27]. Authors in [26] proposed a gait recognition method named localized Grassmann mean representatives with partial least squares regression (LoGPLS), whereas authors in [22] proposed new features called autocorrelation feature where the image at lag time zero is similar to GEI. Another new feature is proposed in [32] and called period energy image (PEI) which is a multi-channel gait template.…”

Section: Literature Reviewmentioning

confidence: 99%

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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Human gait recognition using localized Grassmann mean representatives with partial least squares regression

Cited by 6 publications

References 46 publications

A Novel Multi-Scaled Deep Convolutional Structure for Punctilious Human Gait Authentication

A Novel Multi-Scaled Deep Convolutional Structure for Punctilious Human Gait Authentication

Gait Recognition using Deep Residual Networks

Gait recognition for person re-identification

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