Face Recognition Using Faster R-CNN with Inception-V2 Architecture for CCTV Camera

Detection and prevention of criminal incidents using CCTV are currently increasing trend, for example, car and motorcycle parking lot. However, not continuous people monitoring and careless of events produce useless CCTV function for the prevention of criminal incidents. In this paper, face recognition is used for the recognition of vehicle owners in parking lots that are CCTV installed. The Faster-RCNN method is used for face detection and also for face recognition. Inception V2 architecture is utilized due to has a high accuracy among Convolutional Neural Network architecture. The best learning rate and epoch parameters for the Faster R-CNN model are optimized to improve face recognition on CCTV. In this research, the dataset consists of 6 people images with 50 faces images for each people, which used as training data, testing data, and validation data.