An Appearance Invariant Gait Recognition Technique Using Dynamic Gait Features

Masood, Hajra; Farooq, Humera

doi:10.1155/2021/5591728

Cited by 5 publications

(5 citation statements)

References 37 publications

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“…It makes it possible to learn the complete gait movement law from the movement of the unilateral lower limb through mirror symmetry. And the correlation degree analysis of the left and right lower extremity movements can also be applied to the analysis of clinical pathological gait, such as quantitative evaluation of the recovery degree of the patient's walking ability [ 23 ].…”

Section: Current Status and Methods Of Gait Recognition From Plantar ...mentioning

confidence: 99%

Gait Recognition Based on the Plantar Pressure Data of Ice and Snow Athletes

Li²,

Zhao

et al. 2022

Computational Intelligence and Neuroscience

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The study of plantar pressure has become a research consensus in the field of biomechanics. The purpose of this paper is to study some lower limb movements in the daily activities of ice and snow athletes to obtain relevant data so as to carry out gait recognition analysis research. This paper selects the average foot pressure, forefoot foot pressure, front and rear foot pressure, foot pressure, toe pressure, 2–5 toe pressure, standing with eyes closed, x- and y-axes speed, foot length, foot width, and other actions of ice and snow athletes. Therefore, correlation analysis, work analysis, and curve fitting analysis were carried out on the joint motion in a single gait cycle. The collection and application of foot pressure and foot posture information are also analyzed. According to the plantar structure, the sole is divided into four parts. The maximum pressure point and coordinates of each part, the pressure center point, the ratio of the width and height of the sole of the foot, and so on are extracted as the haptic features of the gait. The experimental data shows that it can be seen that if the plantar area is divided in advance and the weight of each area is marked, whether standing, walking, or standing with one leg closed eyes can achieve better recognition results, and the accuracy rate is all more than 90 percent. The average recognition accuracy rate using the method of dividing four regions is only about 80%, and the accuracy rate of recognition using the method of dividing eight regions is 82%. It can be seen that the features extracted by the FCM model proposed in this paper contain more information of the plantar pressure image, and the accuracy rate is higher in the classification and recognition.

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Section: Current Status and Methods Of Gait Recognition From Plantar ...mentioning

confidence: 99%

Gait Recognition Based on the Plantar Pressure Data of Ice and Snow Athletes

Li²,

Zhao

et al. 2022

Computational Intelligence and Neuroscience

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“…The proposed STPS feature-based gait recognition framework is evaluated on a locally collected South Asian Clothing Variance (SACV) gait dataset [92]. The STPS features are the extension of DGF.…”

Section: Datasetmentioning

confidence: 99%

“…The experiments conducted in [92] backed the adaptation of the dynamic gait features, which are proven to resolve the higher intra-class variance caused by appearance variance.…”

Section: Spatio Temporal Power Spectral Feature-based Gait Recognitionmentioning

confidence: 99%

Utilizing Spatio Temporal Gait Pattern and Quadratic SVM for Gait Recognition

Masood

Farooq

2022

Electronics

Self Cite

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This study aimed to develop a vision-based gait recognition system for person identification. Gait is the soft biometric trait recognizable from low-resolution surveillance videos, where the face and other hard biometrics are not even extractable. The gait is a cycle pattern of human body locomotion that consists of two sequential phases: swing and stance. The gait features of the complete gait cycle, referred to as gait signature, can be used for person identification. The proposed work utilizes gait dynamics for gait feature extraction. For this purpose, the spatio temporal power spectral gait features are utilized for gait dynamics captured through sub-pixel motion estimation, and they are less affected by the subject’s appearance. The spatio temporal power spectral gait features are utilized for a quadratic support vector machine classifier for gait recognition aiming for person identification. Spatio temporal power spectral preserves the spatiotemporal gait features and is adaptable for a quadratic support vector machine classifier-based gait recognition across different views and appearances. We have evaluated the gait features and support vector machine classifier-based gait recognition on a locally collected gait dataset that captures the effect of view variance in high scene depth videos. The proposed gait recognition technique achieves significant accuracy across all appearances and views.

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“…The authors argued that the performance of the RTN network is promising due to their novel optimizing strategy. Masood et al [34] presented the gait verification model with dynamic gait features (DGF) using the subpixel motion estimation technique. The verification process is performed with the Cross-Correlation Score as a feature vector and the SVM (Support Vector machine) classifier.…”

Section: -3-gait Verificationmentioning

confidence: 99%

Deep Learning Based Gait Recognition Using Convolutional Neural Network in the COVID-19 Pandemic

2022

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Gait recognition is the behavioral biometric trait that tracks humans based on their walking motion. It has gained attention because of its non-invasive and unobtrusive behaviors and applicable to the different application area. In this paper, we target model-free gait recognition with the deep learning approach for the Muslim community in the COVID-19 pandemic. The different convolutional neural network architectures (CNN) are examined by using the spatio-temporal gait representation called Gait Energy Images (GEI). We explored both the identification and verification problems to determine the suitability of the proposed CNN frameworks. In gait recognition, the intraclass variation is larger than the inter-class variation because of the shooting view, the walking speed, the wearing condition, and so on. To tackle this challenge, the verification framework is more suitable for the 1:1 association of gait recognition. As for the verification problem, we implemented the Siamese network with the parallel CNN architecture. All the proposed methods are tested against the public gait datasets called OUISIR-LP and OUISIR-MVLP to determine the identification and verification performance in terms of recognition accuracy and error rate. Doi: 10.28991/ESJ-2022-06-05-012 Full Text: PDF

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An Appearance Invariant Gait Recognition Technique Using Dynamic Gait Features

Cited by 5 publications

References 37 publications

Gait Recognition Based on the Plantar Pressure Data of Ice and Snow Athletes

Gait Recognition Based on the Plantar Pressure Data of Ice and Snow Athletes

Utilizing Spatio Temporal Gait Pattern and Quadratic SVM for Gait Recognition

Deep Learning Based Gait Recognition Using Convolutional Neural Network in the COVID-19 Pandemic

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