Exploring Reliable PPG Authentication on Smartwatches in Daily Scenarios

• URL: http://arxiv.org/abs/2503.23930v1
• Date: Mon, 31 Mar 2025 10:25:48 GMT
• Title: Exploring Reliable PPG Authentication on Smartwatches in Daily Scenarios
• Authors: Jiankai Tang, Jiacheng Liu, Renling Tong, Kai Zhu, Zhe Li, Xin Yi, Junliang Xing, Yuanchun Shi, Yuntao Wang,
• Abstract summary: Photoplethysmography ( PPG) sensors are widely deployed in smartwatches and offer a simple and non-invasive authentication approach for daily use.<n>We propose MTL-RAPID, an efficient and reliable PPG authentication model, simultaneously assessing signal quality and verifying user identity.<n>In our comprehensive user studies regarding motion artifacts, time variations, and user preferences, MTL-RAPID achieves a best AUC of 99.2% and an EER of 3.5%, outperforming existing baselines.
• Score: 28.98973058936974
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Photoplethysmography (PPG) Sensors, widely deployed in smartwatches, offer a simple and non-invasive authentication approach for daily use. However, PPG authentication faces reliability issues due to motion artifacts from physical activity and physiological variability over time. To address these challenges, we propose MTL-RAPID, an efficient and reliable PPG authentication model, that employs a multitask joint training strategy, simultaneously assessing signal quality and verifying user identity. The joint optimization of these two tasks in MTL-RAPID results in a structure that outperforms models trained on individual tasks separately, achieving stronger performance with fewer parameters. In our comprehensive user studies regarding motion artifacts (N = 30), time variations (N = 32), and user preferences (N = 16), MTL-RAPID achieves a best AUC of 99.2\% and an EER of 3.5\%, outperforming existing baselines. We opensource our PPG authentication dataset along with the MTL-RAPID model to facilitate future research on GitHub.

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