A Human Gait Recognition Against Information Theft in Smartphone using Residual Convolutional Neural Network Article Swipe
Gogineni Krishna Chaitanya
,
K. Raja Sekhar
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.14569/ijacsa.2020.0110544
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.14569/ijacsa.2020.0110544
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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.14569/ijacsa.2020.0110544
- http://thesai.org/Downloads/Volume11No5/Paper_44-A_Human_Gait_Recognition_against_Information_Theft.pdf
- OA Status
- diamond
- Cited By
- 8
- References
- 21
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3033037852
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- OpenAlex ID
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https://openalex.org/W3033037852Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.14569/ijacsa.2020.0110544Digital Object Identifier
- Title
-
A Human Gait Recognition Against Information Theft in Smartphone using Residual Convolutional Neural NetworkWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2020Year of publication
- Publication date
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2020-01-01Full publication date if available
- Authors
-
Gogineni Krishna Chaitanya, K. Raja SekharList of authors in order
- Landing page
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https://doi.org/10.14569/ijacsa.2020.0110544Publisher landing page
- PDF URL
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https://thesai.org/Downloads/Volume11No5/Paper_44-A_Human_Gait_Recognition_against_Information_Theft.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
- OA URL
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https://thesai.org/Downloads/Volume11No5/Paper_44-A_Human_Gait_Recognition_against_Information_Theft.pdfDirect OA link when available
- Concepts
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Computer science, Biometrics, Convolutional neural network, Authentication (law), Gait, Confidentiality, Identification (biology), Artificial neural network, Artificial intelligence, Iris recognition, Computer security, Human–computer interaction, Machine learning, Physiology, Biology, BotanyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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8Total citation count in OpenAlex
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2025: 1, 2024: 1, 2023: 1, 2022: 2, 2021: 1Per-year citation counts (last 5 years)
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21Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.carried | 118 |
| abstract_inverted_index.defined | 39 |
| abstract_inverted_index.finger, | 50 |
| abstract_inverted_index.genuine | 1, 80 |
| abstract_inverted_index.include | 45 |
| abstract_inverted_index.results | 171 |
| abstract_inverted_index.secured | 138 |
| abstract_inverted_index.Residual | 123 |
| abstract_inverted_index.accuracy | 184 |
| abstract_inverted_index.achieved | 177, 182 |
| abstract_inverted_index.attacks, | 76 |
| abstract_inverted_index.compared | 156 |
| abstract_inverted_index.dataset. | 187 |
| abstract_inverted_index.designed | 91 |
| abstract_inverted_index.emerging | 22 |
| abstract_inverted_index.existing | 64, 159 |
| abstract_inverted_index.features | 23 |
| abstract_inverted_index.geometry | 54 |
| abstract_inverted_index.identify | 108 |
| abstract_inverted_index.internal | 75 |
| abstract_inverted_index.personal | 130 |
| abstract_inverted_index.proposed | 148 |
| abstract_inverted_index.research | 94 |
| abstract_inverted_index.solution | 143 |
| abstract_inverted_index.accuracy, | 179 |
| abstract_inverted_index.accuracy. | 168 |
| abstract_inverted_index.analysing | 32 |
| abstract_inverted_index.approach. | 128 |
| abstract_inverted_index.biometric | 59, 87 |
| abstract_inverted_index.framework | 89 |
| abstract_inverted_index.presented | 140 |
| abstract_inverted_index.prevented | 12 |
| abstract_inverted_index.qualities | 44 |
| abstract_inverted_index.Therefore, | 85 |
| abstract_inverted_index.attributes | 37 |
| abstract_inverted_index.authorized | 99 |
| abstract_inverted_index.biometrics | 25 |
| abstract_inverted_index.continuous | 14 |
| abstract_inverted_index.identified | 7 |
| abstract_inverted_index.impression | 48 |
| abstract_inverted_index.recognized | 30 |
| abstract_inverted_index.simulation | 170 |
| abstract_inverted_index.smartphone | 5, 111, 136 |
| abstract_inverted_index.validation | 60, 88 |
| abstract_inverted_index.behavioural | 36 |
| abstract_inverted_index.biometrics. | 41 |
| abstract_inverted_index.entry-point | 65 |
| abstract_inverted_index.frameworks. | 61 |
| abstract_inverted_index.identifying | 78 |
| abstract_inverted_index.information | 9, 70, 131 |
| abstract_inverted_index.performance | 151 |
| abstract_inverted_index.recognition | 116 |
| abstract_inverted_index.recognizing | 102 |
| abstract_inverted_index.smartphone. | 84 |
| abstract_inverted_index.techniques, | 67 |
| abstract_inverted_index.application. | 26 |
| abstract_inverted_index.confidential | 69 |
| abstract_inverted_index.implementing | 121 |
| abstract_inverted_index.unauthorized | 110, 145 |
| abstract_inverted_index.Convolutional | 124 |
| abstract_inverted_index.architecture. | 149 |
| abstract_inverted_index.differentiate | 97 |
| abstract_inverted_index.physiological | 34, 43 |
| abstract_inverted_index.authentication | 66 |
| abstract_inverted_index.classification | 167 |
| abstract_inverted_index.acknowledgment, | 47 |
| abstract_inverted_index.authentication, | 15 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.7200000286102295 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.61371289 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |