A Highly Reliable PPG Authentication System Based on an Improved AI Model with Dynamic Weighted Triplet Loss Function

Photoplethysmography (PPG) is a convenient and anti-counterfeiting method for identity authentication. However, traditional PPG authentication methods encounter challenges when adding new users, as model adjustments can lead to unstable performance. To address this, we trained a feature embedding model using a loss function to capture feature differences and extract vectors for similarity evaluation. This approach allows our model to recognize new users without adjustments or retraining, ensuring stability and scalability. Additionally, we propose a Dynamic Weighted Triplet Loss (DW Triplet Loss) that considers both distance magnitude and similarity. This enhancement improves distance perception, leading to a more stable similarity evaluation and better threshold determination for class classification. Our model achieves an accuracy of 97.4% and an equal error rate of 2.3% with a low false rejection rate of 4.6% at 1% false acceptance rate, making it suitable for reliable PPG authentication systems.