Intelligent In-Vehicle Safety and Security Monitoring System with Face Recognition
Xiaodi Fu, Jiang Lu, Xin Zhang, Xiaokun Yang, Ishaq Unwala · 2019
Dangerous situations such as children are left in vehicles, are dropped off at wrong stops, or take on wrong school buses usually caused by the negligence of drivers. This paper presents a real-time intelligent in-vehicle monitoring system that can count and recognize people as well as alert drivers if such improprieties or potential dangers happen. The system uses HOG-based face detector from Dlib library to obtain face counting function. Face recognition is achieved through two steps, facial feature extraction and face identification. The ResNet is used in facial feature extraction. It transforms an aligned face into a 256-dimensional vector, a Euclidean facial embedding. In face identification, labeled faces will be transformed to facial embeddings first. Then k-nearest neighbor classifier (kNN) is adopted to identify people using such facial embeddings. The simulation on ChokePoint dataset is tested and the average accuracy is 93 percent.