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.

翻译：光电容积脉搏波传感器广泛部署于智能手表，为日常使用提供了一种简单且非侵入式的身份认证方法。然而，由于身体活动引起的运动伪影和随时间变化的生理差异，PPG身份认证面临可靠性问题。为应对这些挑战，我们提出了MTL-RAPID——一种高效可靠的PPG身份认证模型，该模型采用多任务联合训练策略，同步评估信号质量并验证用户身份。MTL-RAPID中这两个任务的联合优化形成了优于单独任务训练模型的结构，以更少的参数实现了更强的性能。在针对运动伪影（N = 30）、时间变异（N = 32）和用户偏好（N = 16）的全面用户研究中，MTL-RAPID取得了99.2%的最佳AUC和3.5%的EER，性能优于现有基线模型。我们在GitHub上开源了PPG身份认证数据集及MTL-RAPID模型，以促进未来研究。