Driver identification is a key factor in attributing liability for car accident insurance claims and assessing driver competency. Existing driver recognition systems use mechanisms based on identity keys (e.g., car keys and identity cards) or biometric characteristics (e.g., fingerprints, voiceprints, and face recognition). However, identity keys are prone to loss or misappropriation; biometric methods are prone to driver substitution and raise issues pertaining to privacy; and neither approach is applicable to the majority of commercial applications (e.g., hiring delivery drivers and renting out vehicles). This paper presents a novel driver identity recognition system based on the channel state information (CSI) of Wi-Fi signals, which tend to vary with the user, even when performing identical tasks. CSI values corresponding to driver maneuvers (e.g., turning or going straight) are used as inputs for a deep neural network tasked with establishing a driver recognition model. The feasibility of this approach was verified through simulations in the laboratory and with a vehicle, both of which achieved average recognition accuracy of roughly 95%.