Driver Identification System Using Convolutional Neural Network with Background Removal-based Infrared Data Augmentation

초록

As the interest of the autonomous driving increases, techniques related to the advanced driver assistance system are evolving together. In this paper, we propose a novel driver identification system using convolutional neural network (CNN) with the background removal-based infrared image data augmentation. It helps to identify who a driver is, and provides the customized driving environment. The process for the proposed identification system is as follows. First, we acquire customized individual infrared images in a driving simulation environment. Second, we augment the large amount of data by using the background removal-based method and several image processing techniques. Third, the augmented data is trained by the low-complexity-based CNN method. Finally, we load all trained weights to the forward network for real-time processing. In the experimental results, the proposed system had the memory resource of 4,795 KB, which are up to 49.0822 times smaller than benchmark algorithms, and the average F-1 score of 0.9418 for the driver identification accuracy.