User Authentication Recognition Process Using Long Short-Term Memory Model

Abstract

User authentication (UA) is the process by which biometric techniques are used by a person to gain access to a physical or virtual site. UA has been implemented in various applications such as financial transactions, data privacy, and access control. Various techniques, such as facial and fingerprint recognition, have been proposed for healthcare monitoring to address biometric recognition problems. Photoplethysmography (PPG) technology is an optical sensing technique which collects volumetric blood change data from the subject’s skin near the fingertips, earlobes, or forehead. PPG signals can be readily acquired from devices such as smartphones, smartwatches, or web cameras. Classical machine learning techniques, such as decision trees, support vector machine (SVM), and k-nearest neighbor (kNN), have been proposed for PPG identification. We developed a UA classification method for smart devices using long short-term memory (LSTM). Specifically, our UA classifier algorithm uses raw signals so as not to lose the specific characteristics of the PPG signal coming from each user’s specific behavior. In the UA context, false positive and false negative rates are crucial. We recruited thirty healthy subjects and used a smartphone to take PPG data. Experimental results show that our Bi-LSTM-based UA algorithm based on the feature-based machine learning and raw data-based deep learning approaches provides 95.0% and 96.7% accuracy, respectively.