A Continuous Identity Authentication Scheme Based on Physiological and Behavioral Characteristics

Wearable devices have flourished over the past ten years providing great advantages to people and, recently, they have also been used for identity authentication. Most of the authentication methods adopt a one-time authentication manner which cannot provide continuous certification. To address this issue, we present a two-step authentication method based on an own-built fingertip sensor device which can capture motion data (e.g., acceleration and angular velocity) and physiological data (e.g., a photoplethysmography (PPG) signal) simultaneously. When the device is worn on the user's fingertip, it will automatically recognize whether the wearer is a legitimate user or not. More specifically, multisensor data is collected and analyzed to extract representative and intensive features. Then, human activity recognition is applied as the first step to enhance the practicability of the authentication system. After correctly discriminating the motion state, a one-class machine learning algorithm is applied for identity authentication as the second step. When a user wears the device, the authentication process is carried on automatically at set intervals. Analyses were conducted using data from 40 individuals across various operational scenarios. Extensive experiments were executed to examine the effectiveness of the proposed approach, which achieved an average accuracy rate of 98.5% and an F1-score of 86.67%. Our results suggest that the proposed scheme provides a feasible and practical solution for authentication.