DriverID: Driver Identity System Based on Voiceprint and Acoustic Sensing

Abstract

The identification of drivers is essential for many applications, such as attribution of liability for car accidents and driving risk assessment. Most existing driver identification systems adopt identity keys (such as car keys and smart cards) or biometrics technology (such as face recognition, iris recognition, fingerprint recognition, voiceprint recognition, and vein recognition, etc.) to identify drivers. However, these schemes are unable to detect driver changes during a trip. In this paper, combining voiceprint and acoustic driving characteristics, the driver identity system DriverID is proposed to identify the person who is actually driving. DriverID uses the Deep Residual Network (ResNet) to construct an acoustic recognition model based on the voice key recorded by the driver. In addition, the Convolutional Neural Network (CNN) is used to construct an acoustic driving action recognition model based on the reflection of acoustic signals generated by the user. Combining the two recognition methods, DriverID can correctly identify the driver with high probability. It is believed that DriverID is a practical driver identity system.