Car anti-theft systems have evolved from simple mechanical locks to high-end digital solutions. The automotive industry has consistently enhanced features to improve security and user control. This paper presents a facial authentication-based car anti-theft system as a part of a driver monitoring system. The system uses an in-cabin camera for user authentication and a Telegram bot for live alerts. The facial recognition module leverages the Multi-task Cascaded Convolutional Network (MTCNN) model for facial detection and the MobileFaceNet model for facial embedding generation. This module demonstrates superior separability (d-prime value of 3.5126) and the lowest inference time (0.153 s) among the techniques analyzed. The anti-theft system includes a fail-safe door lock, an electronic immobilizer, and a sound alarm system. The proposed system was implemented on a Raspberry Pi 4 for real-time operation.