Vehicle Security by Facial Recognition Using Convolutional Neural Networks

In today's landscape, ensuring vehicle security is crucial, prompting the need for advanced solutions against theft and unauthorized access. This paper proposes integrating deep learning facial recognition with existing security systems to authenticate individuals for access. The approach leverages Convolutional Neural Networks (CNNs) and involves dataset collection, model training, real-time recognition, and system integration. The CNN model is trained extensively on diverse facial images to ensure reliability under various conditions. The paper addresses ethical and practical considerations, such as privacy protection and user acceptance. Ultimately, this fusion of facial recognition with CNNs offers a robust solution to enhance vehicle security and user experience, reducing the risks of unauthorized access and theft. Keywords: Vehicle security, facial recognition, real-time recognition, accuracy, privacy protection.