Learning 3D spatiotemporal gait feature by convolutional network for person identification

Huynh‐The, Thien; Hua, Cam-Hao; Tu, Nguyen Anh; Kim, Dong-Seong

doi:10.1016/j.neucom.2020.02.048

Cited by 45 publications

(9 citation statements)

References 24 publications

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“…Table 4 summarizes the accuracy of our and existing gait recognition techniques. e accuracy of Dynamic Gait Features-based Gait recognition is comparable with existing feature extraction techniques such as GEI [12,17,38] and Gait silhouette [21,22,25]. State of the artwork reported in recent years [12,17,22,25], and [38] are evaluated on CASIA-B dataset and achieved accuracy of 89%, 92.6%, 96%, 99%, and 90.43%, respectively.…”

Section: Comparison With Existing Workmentioning

confidence: 79%

An Appearance Invariant Gait Recognition Technique Using Dynamic Gait Features

Masood

Farooq

2021

International Journal of Optics

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Gait recognition-based person identification is an emerging trend in visual surveillance due to its uniqueness and adaptability to low-resolution video. Existing gait feature extraction techniques such as gait silhouette and Gait Energy Image rely on the human body’s shape. The shape of the human body varies according to the subject’s clothing and carrying conditions. The clothing choice changes every day and results in higher intraclass variance and lower interclass variance. Thus, gait verification and gait recognition are required for person identification. Moreover, clothing choices are highly influenced by the subject’s cultural background, and publicly available gait datasets lack the representation of South Asian Native clothing for gait recognition. We propose a Dynamic Gait Features extraction technique that preserves the spatiotemporal gait pattern with motion estimation. The Dynamic Gait Features under different Use Cases of clothing and carrying conditions are adaptable for gait verification and recognition. The Cross-Correlation score of Dynamic Gait Features resolves the problem of Gait verification. The standard deviation of Cross-Correlation Score lies in the range of 0.12 to 0.2 and reflects a strong correlation in Dynamic Gait Features of the same class. We achieved 98.5% accuracy on Support Vector Machine based gait recognition. Additionally, we develop a multiappearance-based gait dataset that captures the effects of South Asian Native Clothing (SACV-Gait dataset). We evaluated our work on CASIA-B, OUISIR-B, TUM-IITKGP, and SACV-Gait datasets and achieved an accuracy of 98%, 100%, 97.1%, and 98.8%, respectively.

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Section: Comparison With Existing Workmentioning

confidence: 79%

An Appearance Invariant Gait Recognition Technique Using Dynamic Gait Features

Masood

Farooq

2021

International Journal of Optics

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“…Some methods proposed innovative networks with advanced CNN architectures [89] and hybrid CNN-RNN architectures [88] to improve learning efficiency of action discrimination models. Additionally, hand gesture recognition, gait identification, and eye tracking [90], [91] have been considered to improve interactive experiences in XR environments.…”

Section: B Machine Visionmentioning

confidence: 99%

Artificial Intelligence for the Metaverse: A Survey

Huynh‐The¹,

Pham²,

Pham³

et al. 2022

Preprint

Self Cite

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Along with the massive growth of the Internet from the 1990s until now, various innovative technologies have been created to bring users breathtaking experiences with more virtual interactions in cyberspace. Many virtual environments with thousands of services and applications, from social networks to virtual gaming worlds, have been developed with immersive experience and digital transformation, but most are incoherent instead of being integrated into a platform. In this context, metaverse, a term formed by combining meta and universe, has been introduced as a shared virtual world that is fueled by many emerging technologies, such as fifth-generation networks and beyond, virtual reality, and artificial intelligence (AI). Among such technologies, AI has shown the great importance of processing big data to enhance immersive experience and enable humanlike intelligence of virtual agents. In this survey, we make a beneficial effort to explore the role of AI in the foundation and development of the metaverse. We first deliver a preliminary of AI, including machine learning algorithms and deep learning architectures, and its role in the metaverse. We then convey a comprehensive investigation of AI-based methods concerning six technical aspects that have potentials for the metaverse: natural language processing, machine vision, blockchain, networking, digital twin, and neural interface, and being potential for the metaverse. Subsequently, several AI-aided applications, such as healthcare, manufacturing, smart cities, and gaming, are studied to be deployed in the virtual worlds. Finally, we conclude the key contribution of this survey and open some future research directions in AI for the metaverse.

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“…Furthermore, frame-by-frame prediction causes prediction errors because of the similarity of a particular frame with another person’s gait pattern. Another work extracted joint relative distance and joint relative angle features and determined the average and standard deviation of the handcrafted features over 30 frames [ 35 ]. Accumulated features were trained using a convolutional neural network and optimized by the Stochastic Gradient Descent optimizer.…”

Section: Literature Reviewmentioning

confidence: 99%

KinectGaitNet: Kinect-Based Gait Recognition Using Deep Convolutional Neural Network

Bari

Gavrilova

2022

Sensors

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Over the past decade, gait recognition had gained a lot of attention in various research and industrial domains. These include remote surveillance, border control, medical rehabilitation, emotion detection from posture, fall detection, and sports training. The main advantages of identifying a person by their gait include unobtrusiveness, acceptance, and low costs. This paper proposes a convolutional neural network KinectGaitNet for Kinect-based gait recognition. The 3D coordinates of each of the body joints over the gait cycle are transformed to create a unique input representation. The proposed KinectGaitNet is trained directly using the 3D input representation without the necessity of the handcrafted features. The KinectGaitNet design allows avoiding gait cycle resampling, and the residual learning method ensures high accuracy without the degradation problem. The proposed deep learning architecture surpasses the recognition performance of all state-of-the-art methods for Kinect-based gait recognition by achieving 96.91% accuracy on UPCV and 99.33% accuracy on the KGB dataset. The method is the first, to the best of our knowledge, deep learning-based architecture that is based on a unique 3D input representation of joint coordinates. It achieves performance higher than previous traditional and deep learning methods, with fewer parameters and shorter inference time.

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Learning 3D spatiotemporal gait feature by convolutional network for person identification

Cited by 45 publications

References 24 publications

An Appearance Invariant Gait Recognition Technique Using Dynamic Gait Features

An Appearance Invariant Gait Recognition Technique Using Dynamic Gait Features

Artificial Intelligence for the Metaverse: A Survey

KinectGaitNet: Kinect-Based Gait Recognition Using Deep Convolutional Neural Network

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