Multimodal vehicle security system based on internet of things: integration of fingerprint authentication, face recognition, and GPS tracking - Dodik Wahyu Wiratama; Muhammad Iman Nur Hakim; Helmi Wibowo; Arief Novianto (Politeknik Negeri Lhokseumawe, 2026)
The surge in vehicle theft in Indonesia has exposed the weaknesses of conventional security systems such as mechanical keys and immobilizers. This study aims to develop an IoT-based vehicle security system integrating fingerprint authentication, facial recognition, and GPS tracking, and evaluates its performance quantitatively. The system was tested on a Toyota Avanza with six users. Facial recognition used a MobileNetV2 CNN model trained with 1,200 local images across four classes (registered, unregistered, masked, and sunglasses) using a learning rate of 0.001, batch size of 32, and 50 epochs. Fingerprint authentication employed minutiae extraction with Euclidean distance matching. The system successfully implemented two-factor authentication. Facial recognition achieved an accuracy of 94.2%, with a False Acceptance Rate (FAR) of 2.1% and a False Rejection Rate (FRR) of 3.7%. Fingerprint authentication reached 91.5% accuracy, with FAR of 4.3% and FRR of 4.2% under dry finger conditions, while FRR increased to 18.5% for scratched fingers. The system detected unregistered users and triggered engine shutdown while sending photos and GPS coordinates through Telegram. GPS tracking achieved 99.3% positional accuracy. The results demonstrate the feasibility of multimodal IoT-based vehicle security, although performance remains sensitive to lighting conditions, face coverings, and finger surface conditions.