Over the years, logging traffic at checkpoints has been a manual job. Vehicles are needed to be logged for security and validation purposes. This can be automated by using the technology of computer vision and neural networks to recognize the license plates of the vehicles and the driver’s face. Our project is to develop software that uses a camera to recognize the license plate and driver of the vehicle. The developed system initially detects the vehicle and captures the vehicle image. From the image, the portion of the license plate and driver’s face is extracted using image segmentation and then they are searched for recognizable patterns in the database. The details are logged for future reference. By conducting various experiments on our proposed work by taking some sample images collected from the Kaggle dataset and then training the model using these images to identify and detect the vehicle’s number plate accurately and corresponding we try to use the LBPH model to identify the human faces who are inside that vehicle. Our simulation results clearly state that our proposed approach is very accurate in identifying and logging the vehicle and driver’s details into the database.