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.
|Title of host publication||Proceedings of the 2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS)|
|Place of Publication||Piscataway|
|Number of pages||7|
|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||2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS)|
|Abbreviated title||MASS 2022|
|Period||19/10/22 → 23/10/22|
Bibliographical noteGreen 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
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