CONVOLUTION NEURON NETWORK (CNN) AND VGG16 NETWORK ON REAL-TIME FACE RECOGNITION SYSTEM

Abstract

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 7890 faces in real time for 14 persons using a camera with resolution of 1920*1080.
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 framework. Lastly the performance of these two classifiers is
measured using of precision, recall, F1- score and accuracy.