CamPressID: Optimizing Camera Configuration and Finger Pressure for Biometric Authentication

Weizheng Wang; Marek Vette; Qing Wang; Jie Yang; Marco Zuniga

doi:10.1109/MASS56207.2022.00037

CamPressID: Optimizing Camera Configuration and Finger Pressure for Biometric Authentication

Weizheng Wang, Marek Vette, Qing Wang, Jie Yang, Marco Zuniga

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

1 Citation (Scopus)

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Abstract

To protect sensitive information on smartphones, state-of-the-art (SoA) studies exploit the built-in camera to capture PPG signals from fingertips as a hard-to-forge biometric. However, those studies do not provide a comprehensive analysis to optimize the camera parameters and finger pressure, leading to distorted and unstable PPG signals that degrade the authentication performance. To overcome these limitations, we propose the CamPressID framework. First, we analyze various camera parameters and optimize their configuration to obtain PPG signals with a high signal-to-noise ratio. Second, we investigate different finger pressures to identify the best pressure for every subject, in order to avoid signal distortion. To evaluate the performance of CamPressID, we collect a diverse dataset with 58 subjects. Our evaluation results show that CamPressID can improve the average balanced accuracy (BAC) by 10%. Moreover, the BAC reaches 90%, which is similar to the accuracy reported in the SoA using a dedicated PPG sensor for authentication.

Original language	English
Title of host publication	Proceedings of the 2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS)
Editors	Cristina Ceballos
Place of Publication	Piscataway
Publisher	IEEE
Pages	229-235
Number of pages	7
ISBN (Electronic)	978-1-6654-7180-0
ISBN (Print)	978-1-6654-7181-7
DOIs	https://doi.org/10.1109/MASS56207.2022.00037
Publication status	Published - 2022
Event	2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS) - Denver, United States Duration: 19 Oct 2022 → 23 Oct 2022 Conference number: 19th

Conference

Conference	2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS)
Abbreviated title	MASS 2022
Country/Territory	United States
City	Denver
Period	19/10/22 → 23/10/22

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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CamPressID_Optimizing_Camera_Configuration_and_Finger_Pressure_for_Biometric_AuthenticationFinal published version, 968 KB

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@inproceedings{912200fa230e447f92e88ff01bad07c4,

title = "CamPressID: Optimizing Camera Configuration and Finger Pressure for Biometric Authentication",

abstract = "To protect sensitive information on smartphones, state-of-the-art (SoA) studies exploit the built-in camera to capture PPG signals from fingertips as a hard-to-forge biometric. However, those studies do not provide a comprehensive analysis to optimize the camera parameters and finger pressure, leading to distorted and unstable PPG signals that degrade the authentication performance. To overcome these limitations, we propose the CamPressID framework. First, we analyze various camera parameters and optimize their configuration to obtain PPG signals with a high signal-to-noise ratio. Second, we investigate different finger pressures to identify the best pressure for every subject, in order to avoid signal distortion. To evaluate the performance of CamPressID, we collect a diverse dataset with 58 subjects. Our evaluation results show that CamPressID can improve the average balanced accuracy (BAC) by 10%. Moreover, the BAC reaches 90%, which is similar to the accuracy reported in the SoA using a dedicated PPG sensor for authentication.",

author = "Weizheng Wang and Marek Vette and Qing Wang and Jie Yang and Marco Zuniga",

note = "Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; 2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS), MASS 2022 ; Conference date: 19-10-2022 Through 23-10-2022",

year = "2022",

doi = "10.1109/MASS56207.2022.00037",

language = "English",

isbn = "978-1-6654-7181-7",

pages = "229--235",

editor = "Ceballos, {Cristina }",

booktitle = "Proceedings of the 2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS)",

publisher = "IEEE",

address = "United States",

}

Wang, W, Vette, M, Wang, Q , Yang, J & Zuniga, M 2022, CamPressID: Optimizing Camera Configuration and Finger Pressure for Biometric Authentication. in C Ceballos (ed.), Proceedings of the 2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS). IEEE, Piscataway, pp. 229-235, 2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS), Denver, United States, 19/10/22. https://doi.org/10.1109/MASS56207.2022.00037

CamPressID: Optimizing Camera Configuration and Finger Pressure for Biometric Authentication. / Wang, Weizheng; Vette, Marek; Wang, Qing et al.
Proceedings of the 2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS). ed. / Cristina Ceballos. Piscataway: IEEE, 2022. p. 229-235.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - CamPressID

T2 - 2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS)

AU - Wang, Weizheng

AU - Vette, Marek

AU - Wang, Qing

AU - Yang, Jie

AU - Zuniga, Marco

N1 - Conference code: 19th

PY - 2022

Y1 - 2022

N2 - To protect sensitive information on smartphones, state-of-the-art (SoA) studies exploit the built-in camera to capture PPG signals from fingertips as a hard-to-forge biometric. However, those studies do not provide a comprehensive analysis to optimize the camera parameters and finger pressure, leading to distorted and unstable PPG signals that degrade the authentication performance. To overcome these limitations, we propose the CamPressID framework. First, we analyze various camera parameters and optimize their configuration to obtain PPG signals with a high signal-to-noise ratio. Second, we investigate different finger pressures to identify the best pressure for every subject, in order to avoid signal distortion. To evaluate the performance of CamPressID, we collect a diverse dataset with 58 subjects. Our evaluation results show that CamPressID can improve the average balanced accuracy (BAC) by 10%. Moreover, the BAC reaches 90%, which is similar to the accuracy reported in the SoA using a dedicated PPG sensor for authentication.

AB - To protect sensitive information on smartphones, state-of-the-art (SoA) studies exploit the built-in camera to capture PPG signals from fingertips as a hard-to-forge biometric. However, those studies do not provide a comprehensive analysis to optimize the camera parameters and finger pressure, leading to distorted and unstable PPG signals that degrade the authentication performance. To overcome these limitations, we propose the CamPressID framework. First, we analyze various camera parameters and optimize their configuration to obtain PPG signals with a high signal-to-noise ratio. Second, we investigate different finger pressures to identify the best pressure for every subject, in order to avoid signal distortion. To evaluate the performance of CamPressID, we collect a diverse dataset with 58 subjects. Our evaluation results show that CamPressID can improve the average balanced accuracy (BAC) by 10%. Moreover, the BAC reaches 90%, which is similar to the accuracy reported in the SoA using a dedicated PPG sensor for authentication.

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U2 - 10.1109/MASS56207.2022.00037

DO - 10.1109/MASS56207.2022.00037

M3 - Conference contribution

SN - 978-1-6654-7181-7

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EP - 235

BT - Proceedings of the 2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS)

A2 - Ceballos, Cristina

PB - IEEE

CY - Piscataway

Y2 - 19 October 2022 through 23 October 2022

ER -

CamPressID: Optimizing Camera Configuration and Finger Pressure for Biometric Authentication

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