Continuous real-time vehicle driver authentication using convolutional neural network based face recognition

Ekberjan Derman; Albert Ali Salah

doi:10.1109/FG.2018.00092

Continuous real-time vehicle driver authentication using convolutional neural network based face recognition

Ekberjan Derman, Albert Ali Salah

CuteSafe Technology Inc.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

Abstract

Continuous driver authentication is useful in the prevention of car thefts, fraudulent switching of designated drivers, and driving beyond a designated amount of time for a single driver. In this paper, we propose a deep neural network based approach for real time and continuous authentication of vehicle drivers. Features extracted from pre-trained neural network models are classified with support vector classifiers. In order to examine realistic conditions, we collect 130 in-car driving videos from 52 different subjects. We investigate the conditions under which current face recognition technology will allow commercialization of continuous driver authentication.

Original language	English
Title of host publication	Proceedings - 13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018
Publisher	IEEE
Pages	577-584
Number of pages	8
ISBN (Electronic)	9781538623350
DOIs	https://doi.org/10.1109/FG.2018.00092
Publication status	Published - 5 Jun 2018
Event	13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018 - Xi'an, China Duration: 15 May 2018 → 19 May 2018

Conference

Conference	13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018
Country/Territory	China
City	Xi'an
Period	15/05/18 → 19/05/18

Keywords

Convolutional neural network
Driver authentication
Face recognition
In vehicle biometrics
Real time face verification

Access to Document

10.1109/FG.2018.00092

Cite this

@inproceedings{d905fe27f7ad4905bc442598d1d0395c,

title = "Continuous real-time vehicle driver authentication using convolutional neural network based face recognition",

abstract = "Continuous driver authentication is useful in the prevention of car thefts, fraudulent switching of designated drivers, and driving beyond a designated amount of time for a single driver. In this paper, we propose a deep neural network based approach for real time and continuous authentication of vehicle drivers. Features extracted from pre-trained neural network models are classified with support vector classifiers. In order to examine realistic conditions, we collect 130 in-car driving videos from 52 different subjects. We investigate the conditions under which current face recognition technology will allow commercialization of continuous driver authentication.",

keywords = "Convolutional neural network, Driver authentication, Face recognition, In vehicle biometrics, Real time face verification",

author = "Ekberjan Derman and Salah, {Albert Ali}",

year = "2018",

month = jun,

day = "5",

doi = "10.1109/FG.2018.00092",

language = "English",

pages = "577--584",

booktitle = "Proceedings - 13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018",

publisher = "IEEE",

address = "United States",

note = "13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018 ; Conference date: 15-05-2018 Through 19-05-2018",

}

Derman, E & Salah, AA 2018, Continuous real-time vehicle driver authentication using convolutional neural network based face recognition. in Proceedings - 13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018. IEEE, pp. 577-584, 13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018, Xi'an, China, 15/05/18. https://doi.org/10.1109/FG.2018.00092

Continuous real-time vehicle driver authentication using convolutional neural network based face recognition. / Derman, Ekberjan; Salah, Albert Ali.
Proceedings - 13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018. IEEE, 2018. p. 577-584.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

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AU - Salah, Albert Ali

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Y1 - 2018/6/5

N2 - Continuous driver authentication is useful in the prevention of car thefts, fraudulent switching of designated drivers, and driving beyond a designated amount of time for a single driver. In this paper, we propose a deep neural network based approach for real time and continuous authentication of vehicle drivers. Features extracted from pre-trained neural network models are classified with support vector classifiers. In order to examine realistic conditions, we collect 130 in-car driving videos from 52 different subjects. We investigate the conditions under which current face recognition technology will allow commercialization of continuous driver authentication.

AB - Continuous driver authentication is useful in the prevention of car thefts, fraudulent switching of designated drivers, and driving beyond a designated amount of time for a single driver. In this paper, we propose a deep neural network based approach for real time and continuous authentication of vehicle drivers. Features extracted from pre-trained neural network models are classified with support vector classifiers. In order to examine realistic conditions, we collect 130 in-car driving videos from 52 different subjects. We investigate the conditions under which current face recognition technology will allow commercialization of continuous driver authentication.

KW - Convolutional neural network

KW - Driver authentication

KW - Face recognition

KW - In vehicle biometrics

KW - Real time face verification

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M3 - Conference contribution

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Y2 - 15 May 2018 through 19 May 2018

ER -

Continuous real-time vehicle driver authentication using convolutional neural network based face recognition

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Keywords

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