Variation-Stable Fusion for PPG-Based Biometric System

Abstract

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 twosessions. 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].

A Novel PPG-Based Biometric Authentication System Using a Hybrid CVT-ConvMixer Architecture with Dense and Self-Attention Layers

“…Through extending a state-of-the-art algorithm and incorporating DL with various CNN architectures, it addresses challenges like motion artifact compensation and scenario-specific optimizations of prior methods. A CNN model is presented in the study of [ 14 ] to identify unique and time-stable features in PPG data, employing two layers with convolutional kernels, SELU, and dropout, followed by a fully connected layer utilizing sigmoid and binary cross entropy for classification. Incorporating strategies from prior studies, L2 regularization, and 10-fold cross-validation prevent overfitting, while the ADAM optimizer with a 0.0001 learning rate fine-tunes the model over 60 epochs.…”

Time–frequency fusion learning for photoplethysmography biometric recognition

“…In Ref. [21], time‐stable and user‐specific features were developed to design a convolutional neural network (CNN) and execute a variation‐stable approach with three score fusions. Owing to noise and intra‐class variation, the existing combined approaches are sensitive to external factors and are generally applicable to PPG signals in a relatively noise‐free environment.…”