Automatic Gait Recognition using Hybrid Neural Network

Automatic Gait Recognition using Hybrid Neural Network

Drishty

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Jasmeen Gill

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Automatic Gait Recognition using Hybrid Neural Network

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Abstract

Gait is a biometric trait that has been used for user authentication or verification on the basis of various attributes of gait. Gait of an individual get affected due to variation in mood, emotions, age and weight, due to these variation a perfect model is not possible that can be developed so that these all factors can be eliminated. In the proposed work, CASIA dataset has been used as standard dataset. This dataset contains samples of 16 different individuals that have been taken at 0, 45, 90 degrees of angles. Afterwards, silhouette images have been taken for feature extraction from the gait samples using variable2-dimenssiaonl principal component analysis with neural network classifier.

References

17 Cites in Article

Reference Format

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Drishty. 2017. \u201cAutomatic Gait Recognition using Hybrid Neural Network\u201d. Global Journal of Computer Science and Technology - E: Network, Web & Security GJCST-E Volume 17 (GJCST Volume 17 Issue E1): .

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GJCST Volume 17 Issue E1
Pg. 13- 16

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

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Classification

GJCST-E Classification: F.1.1

Submission ReceivedDecember 7, 2016
Peer Review Double Blind
Handling Editor
Accepted January 4, 2017
Published January 15, 2017

Version of record

v1.2

Issue date

March 11, 2017

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Article Matrices

Total Score: 102

Country: India

Subject: Global Journal of Computer Science and Technology - E: Network, Web & Security

Authors: Drishty, Jasmeen Gill (PhD/Dr. count: 0)

View Count (all-time): 266

Total Views (Real + Logic): 6711

Total Downloads (simulated): 1811

Publish Date: 2017 03, Sat

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Total Views: 6711

Total Downloads: 1811

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