Key result
CNN-based fusion for PPG user authentication achieves 100% accuracy in single-session scenarios.
Why the study?
The study investigated the employment of photoplethysmography for user authentication systems.
A variation-stable fusion approach using CNNs for PPG-based biometrics achieved high authentication accuracy, demonstrating potential for user verification systems.
Answers are grounded in this paper and cite their sources.
May enable PPG biometrics in wearables; leaves open multi-session validation in clinical cohorts.
This paper investigates the employment of photoplethysmography (PPG) for user authentication systems. Time-stable and user-specific features are developed by stretching the signal, designing a convolutional neural network and performing a variation-stable approach with three score fusions. Two evaluation scenarios are explored, namely single-session and two-sessions. In the earlier, the training and testing are done solely on one session data to find the user-specific features, while the second scenario is performed on data from two different sessions to test the time permanence of the features. The verification system was tested on four databases achieving an accuracy of 100% for single-session and 87.3% for two-sessions cases. The simulation results confirm the effectiveness of proposed variation-stable fusion which can be extended to other biometrics. The code is available in [1].
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Hwang et al. (2021) studied User authentication. Variation-stable fusion with CNN for PPG was evaluated on Verification system accuracy. A variation-stable fusion approach using a convolutional neural network for PPG-based user authentication achieved an accuracy of 100% for single-session and 87.3% for two-sessions scenarios.
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