Deep features fusion for user authentication based on human activity
Abstract The exponential growth in the use of smartphones means that users must constantly be concerned about the security and privacy of mobile data because the loss of a mobile device could compromise personal information. To address this issue, continuous authentication systems have been proposed...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi-IET
2023-07-01
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| Series: | IET Biometrics |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/bme2.12115 |
| _version_ | 1797420831471566848 |
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| author | Yris Brice Wandji Piugie Christophe Charrier Joël Di Manno Christophe Rosenberger |
| author_facet | Yris Brice Wandji Piugie Christophe Charrier Joël Di Manno Christophe Rosenberger |
| author_sort | Yris Brice Wandji Piugie |
| collection | DOAJ |
| description | Abstract The exponential growth in the use of smartphones means that users must constantly be concerned about the security and privacy of mobile data because the loss of a mobile device could compromise personal information. To address this issue, continuous authentication systems have been proposed, in which users are monitored transparently after initial access to the smartphone. In this study, the authors address the problem of user authentication by considering human activities as behavioural biometric information. The authors convert the behavioural biometric data (considered as time series) into a 2D colour image. This transformation process keeps all the characteristics of the behavioural signal. Time series does not receive any filtering operation with this transformation, and the method is reversible. This signal‐to‐image transformation allows us to use the 2D convolutional networks to build efficient deep feature vectors. This allows them to compare these feature vectors to the reference template vectors to compute the performance metric. The authors evaluate the performance of the authentication system in terms of Equal Error Rate on a benchmark University of Californy, Irvine Human Activity Recognition dataset, and they show the efficiency of the approach. |
| first_indexed | 2024-03-09T07:07:10Z |
| format | Article |
| id | doaj.art-4cf3effaa9b74f01883b856f3ab95011 |
| institution | Directory Open Access Journal |
| issn | 2047-4938 2047-4946 |
| language | English |
| last_indexed | 2024-03-09T07:07:10Z |
| publishDate | 2023-07-01 |
| publisher | Hindawi-IET |
| record_format | Article |
| series | IET Biometrics |
| spelling | doaj.art-4cf3effaa9b74f01883b856f3ab950112023-12-03T09:24:55ZengHindawi-IETIET Biometrics2047-49382047-49462023-07-0112422223410.1049/bme2.12115Deep features fusion for user authentication based on human activityYris Brice Wandji Piugie0Christophe Charrier1Joël Di Manno2Christophe Rosenberger3FIME SAS Caen FranceNormandie University UNICAEN ENSICAEN CNRS GREYC Caen FranceFIME SAS Caen FranceNormandie University UNICAEN ENSICAEN CNRS GREYC Caen FranceAbstract The exponential growth in the use of smartphones means that users must constantly be concerned about the security and privacy of mobile data because the loss of a mobile device could compromise personal information. To address this issue, continuous authentication systems have been proposed, in which users are monitored transparently after initial access to the smartphone. In this study, the authors address the problem of user authentication by considering human activities as behavioural biometric information. The authors convert the behavioural biometric data (considered as time series) into a 2D colour image. This transformation process keeps all the characteristics of the behavioural signal. Time series does not receive any filtering operation with this transformation, and the method is reversible. This signal‐to‐image transformation allows us to use the 2D convolutional networks to build efficient deep feature vectors. This allows them to compare these feature vectors to the reference template vectors to compute the performance metric. The authors evaluate the performance of the authentication system in terms of Equal Error Rate on a benchmark University of Californy, Irvine Human Activity Recognition dataset, and they show the efficiency of the approach.https://doi.org/10.1049/bme2.12115behavioural biometricsbiometric system performance evaluationconvolutional neural netsfeature extractiongait analysisgait biometrics |
| spellingShingle | Yris Brice Wandji Piugie Christophe Charrier Joël Di Manno Christophe Rosenberger Deep features fusion for user authentication based on human activity IET Biometrics behavioural biometrics biometric system performance evaluation convolutional neural nets feature extraction gait analysis gait biometrics |
| title | Deep features fusion for user authentication based on human activity |
| title_full | Deep features fusion for user authentication based on human activity |
| title_fullStr | Deep features fusion for user authentication based on human activity |
| title_full_unstemmed | Deep features fusion for user authentication based on human activity |
| title_short | Deep features fusion for user authentication based on human activity |
| title_sort | deep features fusion for user authentication based on human activity |
| topic | behavioural biometrics biometric system performance evaluation convolutional neural nets feature extraction gait analysis gait biometrics |
| url | https://doi.org/10.1049/bme2.12115 |
| work_keys_str_mv | AT yrisbricewandjipiugie deepfeaturesfusionforuserauthenticationbasedonhumanactivity AT christophecharrier deepfeaturesfusionforuserauthenticationbasedonhumanactivity AT joeldimanno deepfeaturesfusionforuserauthenticationbasedonhumanactivity AT christopherosenberger deepfeaturesfusionforuserauthenticationbasedonhumanactivity |