Face recognition in dense crowd using deep learning approaches with IP camera

Abstract

A facial recognition system is a biometric security and surveillance system that can identify and monitor individuals in a crowded area. Manually monitoring a crowded environment is a difficult and error-prone task. Therefore, in such contexts, a model that automatically detects and recognises people's faces is needed to improve security. The automation of face recognition brings the benefit of a more efficient and accurate solution. This paper proposes an advanced model that has the ability to detect and recognise faces in dense crowds by using deep learning techniques. Where the input is live video, the process involves splitting the video into frames and each frame is fed into the model. The Multi-Task Cascaded Convolutional Neural Networks (MTCNN) algorithm is used for face detection. It accurately locates faces in frames and images and generates boundaries around the faces as output. The detected faces are then fed as input to a model, where they are compared with data from the database. If a face is recognised, the name of the recognised person is displayed in the boundary box of the frame, otherwise it is displayed that the person is unknown. FaceNet is used for face recognition tasks.

Keywords

• face detection
• multi-task cascaded convolutional neural networks
• dense crowd
• FaceNet