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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

A Human Gait Recognition Against Information Theft in Smartphone using Residual Convolutional Neural Network

Author 1: Gogineni Krishna Chaitanya
Author 2: Krovi.Raja Sekhar

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2020.0110544

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 5, 2020.

  • Abstract and Keywords
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Abstract: The genuine user of the smartphone is identified and information theft is prevented by continuous authentication, which is one of the most emerging features in biometrics application. A person is recognized by analysing the physiological or behavioural attributes is defined as biometrics. The physiological qualities include iris acknowledgment, impression of finger, palm and face geometry are used in the biometric validation frameworks. In the existing entry-point authentication techniques, a confidential information is lost because of internal attacks, while identifying the genuine user of the smartphone. Therefore, a biometric validation framework is designed in this research study to differentiate an authorized user by recognizing the gait. In order to identify the unauthorized smartphone access, a human gait recognition is carried out by implementing a Residual Convolutional Neural Network (RCNN) approach. A personal information of end user in smartphone is secured and presented a better solution from unauthorized access by proposed architecture. The performance of RCNN method is compared with the existing Deep Neural Network (DNN) in terms of classification accuracy. The simulation results showed that the RCNN method achieved 98.15% accuracy, where DNN achieved 95.67% accuracy on OU-ISIR dataset.

Keywords: Authentication; biometric analysis; genuine user; information loss; residual convolutional neural network; smartphone

Gogineni Krishna Chaitanya and Krovi.Raja Sekhar, “A Human Gait Recognition Against Information Theft in Smartphone using Residual Convolutional Neural Network” International Journal of Advanced Computer Science and Applications(IJACSA), 11(5), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110544

@article{Chaitanya2020,
title = {A Human Gait Recognition Against Information Theft in Smartphone using Residual Convolutional Neural Network},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110544},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110544},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {5},
author = {Gogineni Krishna Chaitanya and Krovi.Raja Sekhar}
}


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