HeartID-based authentication for autonomous vehicles using deep learning and random number generators

Nima Karimian; Fatemeh Tehranipoor

doi:10.1117/12.2519255

13 May 2019 HeartID-based authentication for autonomous vehicles using deep learning and random number generators

Nima Karimian, Fatemeh Tehranipoor

Proceedings Volume 10993, Mobile Multimedia/Image Processing, Security, and Applications 2019; 109930E (2019) https://doi.org/10.1117/12.2519255
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

HeartID biometric authentication technology is integrated into the multi-faceted steering wheel and car seat, allowing only authorized personnel to operate the vehicle, with access to the vehicle's connected devices and computers. The application of this HearID will be used for law enforcement and ride-sharing services where the person can access the car using keyless entry technology. In this study, we investigate the possibility of incorporating human heart signal called ECG into autonomous cars. Our platform can facilitate a secure authentication for end users using their heart signal to enable entry to the car. In this paper, we have presented the ECG-based biometric authentication for connecting autonomous vehicle that can act as an interface between humans and sensors for authentication purposes. In this study, we turn the ECG noise into the good feature where the noise is used for random number generators with high entropy. For evaluation of HeartID, NIST test suit is applied to evaluate the randomness of TRNG.

Citation Download Citation

Nima Karimian and Fatemeh Tehranipoor "HeartID-based authentication for autonomous vehicles using deep learning and random number generators", Proc. SPIE 10993, Mobile Multimedia/Image Processing, Security, and Applications 2019, 109930E (13 May 2019); https://doi.org/10.1117/12.2519255

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KEYWORDS

Electrocardiography

Biometrics

Heart

Interference (communication)

Reliability

Unmanned vehicles

Feature extraction

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