Multiview Gait Recognition on Unconstrained Path Using Graph Convolutional Neural Network

Shopon,; Hsu, Gee-Sern; Gavrilova, Marina L.

doi:10.1109/access.2022.3176873

Cited by 7 publications

(17 citation statements)

References 62 publications

(56 reference statements)

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“…They can also take temporal information into account by processing sequences of graphs. This makes GCNs a suitable choice for recognizing gaits with varying speeds and styles [ 49 , 127 , 128 ].…”

Section: Taxonomymentioning

confidence: 99%

“…However, despite the growing interest and advancements in gait recognition, there are still several challenges and limitations that need to be addressed. One of the main challenges is the significant variation in gait caused by individual differences, clothing, carrying conditions, and walking speeds [ 49 , 174 , 177 ]. Additionally, the quality of the input data, such as the resolution, illumination, and occlusion, can significantly affect the performance of gait recognition systems.…”

Section: Limitations and Challengesmentioning

confidence: 99%

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Person Recognition Based on Deep Gait: A Survey

Khaliluzzaman

Uddin

Deb

et al. 2023

Sensors

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Gait recognition, also known as walking pattern recognition, has expressed deep interest in the computer vision and biometrics community due to its potential to identify individuals from a distance. It has attracted increasing attention due to its potential applications and non-invasive nature. Since 2014, deep learning approaches have shown promising results in gait recognition by automatically extracting features. However, recognizing gait accurately is challenging due to the covariate factors, complexity and variability of environments, and human body representations. This paper provides a comprehensive overview of the advancements made in this field along with the challenges and limitations associated with deep learning methods. For that, it initially examines the various gait datasets used in the literature review and analyzes the performance of state-of-the-art techniques. After that, a taxonomy of deep learning methods is presented to characterize and organize the research landscape in this field. Furthermore, the taxonomy highlights the basic limitations of deep learning methods in the context of gait recognition. The paper is concluded by focusing on the present challenges and suggesting several research directions to improve the performance of gait recognition in the future.

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Section: Taxonomymentioning

confidence: 99%

Section: Limitations and Challengesmentioning

confidence: 99%

Person Recognition Based on Deep Gait: A Survey

Khaliluzzaman

Uddin

Deb

et al. 2023

Sensors

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“…Hung-Min et al [27], Liao et al [48], Tianrui et al [62], Shopon et al [40] and Muhammad et al [36]:…”

Section: Related Work a Human Gait Recognitionmentioning

confidence: 99%

“…[40] introduced the RGCNN architecture (Residual Connection-based Graph Convolutional Neural Network). In fact, RGCNN backbone based on Residual Connections resulted in transformed body joints.…”

mentioning

confidence: 99%

Biometric-Based Security System for Smart Riding Clubs

et al. 2022

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Horses, workers or riders need safety in a farm or a riding club. On account of the great value of the horse, the breeder needs to protect it from theft and disease. In this context, it is important to detect and recognize the identity of each worker or rider and horse for security reasons. In fact, this paper proposes a Smart Riding Club Biometric System (SRCBS) consisting in automatically detecting and recognizing horses as well as humans. The proposed system is based on the facial biometrics for a horse as well as the human gait biometrics due to their simplicity and intuitiveness in an uncontrolled environment. This work suggests a Siamese network based on DenseNet features for human gait recognition, a Sparse Neural Network (SNN) based on sparse features for horse face detection and a horse face recognition method based on Gabor features, LDA and SVM. Because of the unavailability of horse databases, this paper presents a new benchmark for horse detection and recognition in order to evaluate our proposed system. The proposed systems achieved an average accuracy of gait recognition equal to 95% in the 0°view, 100% in the 90°view and 98.90% in the 180°view on Casia-B dataset, an average precision equal to 90% for horse face detection and a recognition rate equal to 99.89% for horse face identification.INDEX TERMS Horse face detection, human gait recognition, neural network, siamese network, smart riding club, THDD.

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“…Gait feature extraction, however, may be performed with lower-quality photos or movies. The two most frequent types of gait recognition systems [5,6] hip's angular motion to create a motion model. Using data on the positions of the joints obtained from a Microsoft Kinect device, a 3D skeleton model is reconstructed to infer the gait characteristics.…”

Section: Introductionmentioning

confidence: 99%

CDBMGCIG: Design of a Cross-Domain Bioinspired Model for identification of Gait Components via Iterated GANs

Misal,

Choubey,

Choubey

et al. 2023

IJRITCC

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This Gait identification assists in recognition of human body components from temporal image sequences. Such components consist of connected-body entities including head, upper body, lower body regions. Existing Gait recognition models use deep learning methods including variants of Convolutional Neural Networks (CNNs), Q-Learning, etc. But these methods are either highly complex, or do not perform well under complex background conditions. Moreover, most of these models are validated on a specific environmental condition, and cannot be scaled for general-purpose deployments. To overcome these issues, this text proposes design of a novel cross-domain bioinspired model for identification of gait components via Iterated Generative Adversarial Networks (IGANs). The proposed model initially extracts multidomain pixel-level feature sets from different images. These include frequency components via Fourier analysis, entropy components via Cosine analysis, spatial components via Gabor analysis, and window-based components via Wavelet &Convolutional analysis. These feature sets are processed via a Grey Wolf Optimization (GWO) Model, which assists in identification of high-density & highly variant features for different gait components. These features are classified via an iterated GAN, which comprises of Generator & Discriminator ssModels that assist in evaluating connected body components. These operations generate component-level scores that assist in identification of gait from complex background images. Due to which, the proposed model was observed to achieve 9.5% higher accuracy, 3.4% higher precision, and 2.9% higher recall than existing gait identification methods. The model also uses iterative learning, due to which its accuracy is incrementally improved w.r.t. number of evaluated image sets.

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Multiview Gait Recognition on Unconstrained Path Using Graph Convolutional Neural Network

Cited by 7 publications

References 62 publications

Person Recognition Based on Deep Gait: A Survey

Person Recognition Based on Deep Gait: A Survey

Biometric-Based Security System for Smart Riding Clubs

CDBMGCIG: Design of a Cross-Domain Bioinspired Model for identification of Gait Components via Iterated GANs

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