Neural Networks (CNNs) and Vgg on Real Time Face Recognition System

Face Recognition is considered as a heavily studied topic in computer vision field. The capability to automatically identify and authenticate human's faces using real-time images is an important aspect in surveillance, security, and other related domains. There are separate applications that help in identifying individuals at specific locations which help in detecting intruders. The real-time recognition is mandatory for surveillance purposes. A number of machine learning methods along with classifiers are used for the recognition of faces. This work introduces a new real time face Recognition system. The process is broken into four major steps: (1) database collection, (2) face recognition to identify particular persons and (3) Performance evaluation. For the first step, the system collects 1056 faces in real time for 24 persons using a camera with resolution of 112*92.Second step, efficient real time face recognition algorithm is then used to recognize faces with a known database. For real time face Recognition, VGG-16 with Transfer Learning and  Convolutional Neural Network (CNN) are used. This proposed system is implemented using keras. Lastly the performance of these two classifiers is measured using of precision, recall, F1-score, and accuracy.