GaitVision: Real-Time Extraction of Gait Parameters Using Residual Attention Network

Gait walking patterns are one of the key research topics in natural biometrics. The temporal information of the unique gait sequence of a person is preserved and used as a powerful data for access. Often there is a dive into the flexibility of gait sequence due to unstructured and unnecessary sequen...

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Main Authors: Mohammad Farukh Hashmi, B. Kiran Kumar Ashish, Prabhu Chaitanya, Avinash Keskar, Sinan Q. Salih, Neeraj Dhanraj Bokde
Format: Article
Language:English
Published: Wiley 2021-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2021/1589716
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author Mohammad Farukh Hashmi
B. Kiran Kumar Ashish
Prabhu Chaitanya
Avinash Keskar
Sinan Q. Salih
Neeraj Dhanraj Bokde
author_facet Mohammad Farukh Hashmi
B. Kiran Kumar Ashish
Prabhu Chaitanya
Avinash Keskar
Sinan Q. Salih
Neeraj Dhanraj Bokde
author_sort Mohammad Farukh Hashmi
collection DOAJ
description Gait walking patterns are one of the key research topics in natural biometrics. The temporal information of the unique gait sequence of a person is preserved and used as a powerful data for access. Often there is a dive into the flexibility of gait sequence due to unstructured and unnecessary sequences that tail off the necessary sequence constraints. The authors in this work present a novel perspective, which extracts useful gait parameters regarded as independent frames and patterns. These patterns and parameters mark as unique signature for each subject in access authentication. This information extracted learns to identify the patterns associated to form a unique gait signature for each person based on their style, foot pressure, angle of walking, angle of bending, acceleration of walk, and step-by-step distance. These parameters form a unique pattern to plot under unique identity for access authorization. This sanitized data of patterns is further passed to a residual deep convolution network that automatically extracts the hierarchical features of gait pattern signatures. The end layer comprises of a Softmax classifier to classify the final prediction of the subject identity. This state-of-the-art work creates a gait-based access authentication that can be used in highly secured premises. This work was specially designed for Defence Department premises authentication. The authors have achieved an accuracy of 90%±1.3% in real time. This paper mainly focuses on the assessment of the crucial features of gait patterns and analysis of gait patterns research.
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publishDate 2021-01-01
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spelling doaj-art-229c37718d0b4237b02c525b219488692025-02-03T01:07:05ZengWileyComplexity1099-05262021-01-01202110.1155/2021/1589716GaitVision: Real-Time Extraction of Gait Parameters Using Residual Attention NetworkMohammad Farukh Hashmi0B. Kiran Kumar Ashish1Prabhu Chaitanya2Avinash Keskar3Sinan Q. Salih4Neeraj Dhanraj Bokde5Department of Electronics and Communication EngineeringComputer VisionUniversity of North TexasDepartment of Electronics and Communication EngineeringInstitute of Research and DevelopmentDepartment of Mechanical and Production Engineering-Renewable Energy and ThermodynamicsGait walking patterns are one of the key research topics in natural biometrics. The temporal information of the unique gait sequence of a person is preserved and used as a powerful data for access. Often there is a dive into the flexibility of gait sequence due to unstructured and unnecessary sequences that tail off the necessary sequence constraints. The authors in this work present a novel perspective, which extracts useful gait parameters regarded as independent frames and patterns. These patterns and parameters mark as unique signature for each subject in access authentication. This information extracted learns to identify the patterns associated to form a unique gait signature for each person based on their style, foot pressure, angle of walking, angle of bending, acceleration of walk, and step-by-step distance. These parameters form a unique pattern to plot under unique identity for access authorization. This sanitized data of patterns is further passed to a residual deep convolution network that automatically extracts the hierarchical features of gait pattern signatures. The end layer comprises of a Softmax classifier to classify the final prediction of the subject identity. This state-of-the-art work creates a gait-based access authentication that can be used in highly secured premises. This work was specially designed for Defence Department premises authentication. The authors have achieved an accuracy of 90%±1.3% in real time. This paper mainly focuses on the assessment of the crucial features of gait patterns and analysis of gait patterns research.http://dx.doi.org/10.1155/2021/1589716
spellingShingle Mohammad Farukh Hashmi
B. Kiran Kumar Ashish
Prabhu Chaitanya
Avinash Keskar
Sinan Q. Salih
Neeraj Dhanraj Bokde
GaitVision: Real-Time Extraction of Gait Parameters Using Residual Attention Network
Complexity
title GaitVision: Real-Time Extraction of Gait Parameters Using Residual Attention Network
title_full GaitVision: Real-Time Extraction of Gait Parameters Using Residual Attention Network
title_fullStr GaitVision: Real-Time Extraction of Gait Parameters Using Residual Attention Network
title_full_unstemmed GaitVision: Real-Time Extraction of Gait Parameters Using Residual Attention Network
title_short GaitVision: Real-Time Extraction of Gait Parameters Using Residual Attention Network
title_sort gaitvision real time extraction of gait parameters using residual attention network
url http://dx.doi.org/10.1155/2021/1589716
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AT prabhuchaitanya gaitvisionrealtimeextractionofgaitparametersusingresidualattentionnetwork
AT avinashkeskar gaitvisionrealtimeextractionofgaitparametersusingresidualattentionnetwork
AT sinanqsalih gaitvisionrealtimeextractionofgaitparametersusingresidualattentionnetwork
AT neerajdhanrajbokde gaitvisionrealtimeextractionofgaitparametersusingresidualattentionnetwork