Use of deep-learning in transport services for maintaining safety and regulating unauthorized drivers

Abstract

Research from WHO shows in every 1.44 minutes a teenager is killed in road traffic crashes around the world. Our paper suggests the use of Deep Learning Algorithms in preventing such casualties through hardware and software implementation of the device in motor vehicles, also to overcome the potential limitation of Face Recognition to go that extra mile. In this paper we suggest using the integration of 4 models namely, Face Detection, Passive Liveness Detection (PLD), Face Recognition, Eye Detection. The Face Detection model consists of 2 versions which use Haar Cascades and Histogram of Oriented Gradients (HOG). PLD model is used for Presentation Attack Detection. The face recognition model is trained using the shape predictor 68 landmarks which are unique for each face. Using these landmarks, eye detection is also performed and the monitoring of sleepiness is carried out with the overall results of 98% accuracy with the face detection model, 94% accuracy with the face recognition model (limited to n=10 faces per model) with an Eye Aspect Ratio threshold of 0.3. Along with that, different current attack scenarios/ limitations of Facial Recognition that will be faced with these devices are described. Based on these scenarios, some of the preventive methods are elaborated to make the purpose of the device to its fullest performance.