# Exploring Reliable PPG Authentication on Smartwatches in Daily Scenarios

arXiv: https://arxiv.org/abs/2503.23930 · HTML: https://mlsanity.com/paper/2503.23930/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 60 references that are in the map.
- First posted on arXiv 31 Mar 2025.

## Builds on (most cited first, 12 of 12)

- [InceptionTime: Finding AlexNet for Time Series Classification](https://mlsanity.com/paper/1909.04939/) (2019)
- [NDDR-CNN: Layerwise Feature Fusing in Multi-Task CNNs by Neural Discriminative Dimensionality Reduction](https://mlsanity.com/paper/1801.08297/) (2018)
- [HyperFace: A Deep Multi-task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition](https://mlsanity.com/paper/1603.01249/) (2016)
- [MMPD: Multi-Domain Mobile Video Physiology Dataset](https://mlsanity.com/paper/2302.03840/) (2023)
- [Spiking-PhysFormer: Camera-Based Remote Photoplethysmography with Parallel Spike-driven Transformer](https://mlsanity.com/paper/2402.04798/) (2024)
- [Evaluation of PPG Biometrics for Authentication in different states](https://mlsanity.com/paper/1712.08583/) (2017)
- [Active User Authentication for Smartphones: A Challenge Data Set and Benchmark Results](https://mlsanity.com/paper/1610.07930/) (2016)
- [Summit Vitals: Multi-Camera and Multi-Signal Biosensing at High Altitudes](https://mlsanity.com/paper/2409.19223/) (2024)
- [Camera-Based Remote Physiology Sensing for Hundreds of Subjects Across Skin Tones](https://mlsanity.com/paper/2404.05003/) (2024)
- [A Survey on Behavioral Biometric Authentication on Smartphones](https://mlsanity.com/paper/1801.09308/) (2018)
- [This PIN Can Be Easily Guessed: Analyzing the Security of Smartphone Unlock PINs](https://mlsanity.com/paper/2003.04868/) (2020)
- [VitalVideos-Europe: A dataset of face videos with PPG and blood pressure ground truths](https://mlsanity.com/paper/2306.11891/) (2023)

## Built on by (most cited first, 1 of 1)

- [AccLock: Unlocking Identity with Heartbeat Using In-Ear Accelerometers](https://mlsanity.com/paper/2605.11901/) (2026)

## Abstract (arXiv)

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.