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In-Car Driver Authentication Using Wireless Sensing

Citation Author(s):
Beibei Wang
Submitted by:
Sai Deepika Regani
Last updated:
13 May 2019 - 11:17am
Document Type:
Poster
Document Year:
2019
Event:
Presenters Name:
Sai Deepika Regani
Paper Code:
3556

Abstract 

Abstract: 

Automobiles have become an essential part of everyday lives. In this work, we attempt to make them smarter by introducing the idea of in-car driver authentication using wireless sensing. Our aim is to develop a model which can recognize drivers automatically. Firstly, we address the problem of "changing in-car environments", where the existing wireless sensing based human identification system fails. To this end, we build the first in-car driver radio biometric dataset to understand the effect of changing environments on human radio biometrics. This dataset consists of radio biometrics of five people collected over a period of two months. We leverage this dataset-to create machine learning (ML) models that make the proposed system adaptive to new in-car environments. We obtained a maximum accuracy of 99.3% in classifying two drivers and 90.66% accuracy in validating a single driver.

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Dataset Files

ICASSP_conf_poster_driver_authentication.pdf

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