# A Lightweight Convolutional Neural Network Method for Two-Dimensional PhotoPlethysmoGraphy Signals
###### Abstract  
Data information security on wearable devices has emerged as a significant concern among users, so it becomes urgent to explore authentication methods based on wearable devices. Using PhotoPlethysmoGraphy (PPG) signals for identity authentication has been proven effective in biometric authentication. This paper proposes a convolutional neural network authentication method based on 2D PPG signals applied to wearable devices. This method uses Markov Transition Field technology to convert one-dimensional PPG signal data into two-dimensional image data, which not only retains the characteristics of the signal but also enriches the spatial information. Afterward, considering that wearable devices usually have limited resources, a lightweight convolutional neural network model is also designed in this method, which reduces resource consumption and computational complexity while ensuring high performance. It is proved experimentally that this method achieves 98.62% and 96.17% accuracy on the training set and test set, respectively, an undeniable advantage compared to the traditional one-dimensional deep learning method and the classical two-dimensional deep learning method. wearable devices; PPG signals; Markov Transition Field; lightweight convolutional neural network +
Footnote †: journal: Applied sciences  
additional operations, which makes continuous user authentication more natural. At the same time, the PPG signal can continuously monitor blood circulation, thereby achieving continuous user identity verification and improving the accuracy and reliability of identity authentication. PPG signals meet the essential characteristics of biometrics: universality, persistence, uniqueness, and ease of collection. Therefore, using PPG signals as biometrics to research identity authentication methods is feasible and can bring new possibilities to biometric identity authentication. Consequently, this study proposes a deep learning-based identity authentication method for PPG signals. The primary contributions are as follows: firstly, the filtered one-dimensional temporal PPG data is partitioned into individual cycles and transformed into two-dimensional images using the Markov Transition Field (MTF) technique [5]. Unlike most existing identity authentication methods that directly employ one-dimensional PPG signals as inputs, this paper initially converts the one-dimensional temporal data into two-dimensional image data before incorporating it into a convolutional neural network (CNN). Two-dimensional image data is richer than one-dimensional time series data because of its multi-dimensional spatial information. Especially in color images, the three independent channels of red, green, and blue each carry different image information. Therefore, in feature extraction by convolutional neural networks, two-dimensional image data shows incomparable advantages. Secondly, a lightweight CNN model is devised, integrating the concepts of Depthwise Separable Convolution [6] and Residual Structures [7]. This model maintains high recognition accuracy while reducing memory consumption and improving runtime efficiency. It is well-suited for resource-constrained devices such as wearable devices, which have small form factors, low power requirements, and limited computational resources. Identity authentication can be approached as a classification problem to gain deeper insights. Experimental results show that the lightweight CNN model proposed in this study achieves an accuracy rate of 98.62% on the training set and 96.17% on the testing set, both surpassing some traditional deep learning methods. Hence, this research demonstrates high feasibility. The use of PPG signals for biometric identification has garnered significant attention and application in the current literature. Researchers have actively explored methods that use PPG signals to distinguish between individuals by extracting unique and stable features from these signals. Two primary categories of methods are available for use.