Using ResNet Transfer Deep Learning Methods in Person Identification According to Physical Actions

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dc.authoridkilic, safak/0000-0002-2014-7638
dc.contributor.authorKilic, Safak
dc.contributor.authorAskerzade, Iman
dc.contributor.authorKaya, Yilmaz
dc.date.accessioned2024-12-24T19:28:32Z
dc.date.available2024-12-24T19:28:32Z
dc.date.issued2020
dc.departmentSiirt Üniversitesi
dc.description.abstractToday, biometric technologies are one of the areas of information security which are increasingly used in all areas required by human security. The subjects such as person identification (PI), age prediction, and gender recognition are among the topics of human-computer interactivity that have been commonly researched in both academic and other areas in recent years. PI is the process of identifying the person according to biometric features obtained. In this study, the PI process was carried out with ResNet transfer deep learning methods by using the signals from an accelerometer, magnetometer and gyroscope sensors attached to 5 different regions of the persons. Here, the persons were identified depending on different physical actions and effective actions in the PI were determined. Furthermore, the effective body areas have also been identified in PI. Generally, high success rates have been observed through ResNet architecture. This study has shown that the signals of wearable accelerometer, gyroscope, magnetometer sensors can be used as a new biometric system to prevent identity fraud attacks. In summary, the proposed method can be greatly beneficial for the effective use of wearable sensor signals in biometric applications.
dc.identifier.doi10.1109/ACCESS.2020.3040649
dc.identifier.endpage220373
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85097925247
dc.identifier.scopusqualityQ1
dc.identifier.startpage220364
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2020.3040649
dc.identifier.urihttps://hdl.handle.net/20.500.12604/7111
dc.identifier.volume8
dc.identifier.wosWOS:000600300400001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIEEE-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIeee Access
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241222
dc.subjectBiometrics (access control)
dc.subjectIdentification of persons
dc.subjectSensors
dc.subjectDeep learning
dc.subjectMagnetic sensors
dc.subjectAccelerometers
dc.subjectObject recognition
dc.subjectTransfer deep learning models
dc.subjectResNet
dc.subjectperson identification
dc.subjectwearable sensor
dc.subjectbiometric system
dc.titleUsing ResNet Transfer Deep Learning Methods in Person Identification According to Physical Actions
dc.typeArticle

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