# Evaluation of PPG Biometrics for Authentication in different states

arXiv: https://arxiv.org/abs/1712.08583 · HTML: https://mlsanity.com/paper/1712.08583/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2017.

## Built on by (most cited first, 13 of 13)

- [A Scoping Review of Deep Learning Methods for Photoplethysmography Data](https://mlsanity.com/paper/2401.12783/) (2024)
- [Adversarial Attacks and Defenses in Physiological Computing: A Systematic Review](https://mlsanity.com/paper/2102.02729/) (2021)
- [EDITH :ECG biometrics aided by Deep learning for reliable Individual auTHentication](https://mlsanity.com/paper/2102.08026/) (2021)
- [ECG Unveiled: Analysis of Client Re-identification Risks in Real-World ECG Datasets](https://mlsanity.com/paper/2408.10228/) (2024)
- [Opportunistic Multi-Modal User Authentication for Health-Tracking IoT Wearables](https://mlsanity.com/paper/2109.13705/) (2021)
- [Your smartphone could act as a pulse-oximeter and as a single-lead ECG](https://mlsanity.com/paper/2305.12583/) (2023)
- [Know Me by My Pulse: Toward Practical Continuous Authentication on Wearable Devices via Wrist-Worn PPG](https://mlsanity.com/paper/2508.13690/) (2025)
- [Hiding Your Signals: A Security Analysis of PPG-based Biometric Authentication](https://mlsanity.com/paper/2207.04434/) (2022)
- [OmniBuds: A Sensory Earable Platform for Advanced Bio-Sensing and On-Device Machine Learning](https://mlsanity.com/paper/2410.04775/) (2024)
- [Exploring Reliable PPG Authentication on Smartwatches in Daily Scenarios](https://mlsanity.com/paper/2503.23930/) (2025)
- [CardioID: Mitigating the Effects of Irregular Cardiac Signals for Biometric Identification](https://mlsanity.com/paper/2203.16381/) (2022)
- [BIOWISH: Biometric Recognition using Wearable Inertial Sensors detecting Heart Activity](https://mlsanity.com/paper/2210.09843/) (2022)
- [AccLock: Unlocking Identity with Heartbeat Using In-Ear Accelerometers](https://mlsanity.com/paper/2605.11901/) (2026)

## Abstract (arXiv)

Amongst all medical biometric traits, Photoplethysmograph (PPG) is the easiest to acquire. PPG records the blood volume change with just combination of Light Emitting Diode and Photodiode from any part of the body. With IoT and smart homes' penetration, PPG recording can easily be integrated with other vital wearable devices. PPG represents peculiarity of hemodynamics and cardiovascular system for each individual. This paper presents non-fiducial method for PPG based biometric authentication. Being a physiological signal, PPG signal alters with physical/mental stress and time. For robustness, these variations cannot be ignored. While, most of the previous works focused only on single session, this paper demonstrates extensive performance evaluation of PPG biometrics against single session data, different emotions, physical exercise and time-lapse using Continuous Wavelet Transform (CWT) and Direct Linear Discriminant Analysis (DLDA). When evaluated on different states and datasets, equal error rate (EER) of 0.5%-6% was achieved for 45-60s average training time. Our CWT/DLDA based technique outperformed all other dimensionality reduction techniques and previous work.