Aging face verification using deep learning

Abstract

The era of technological security has grown more attention and interest than ever in the past few years. From wired video surveillance and passcodes, to wireless cameras and facial recognition. Over the years Deep learning has seen a high rate of improvement and its approaches for facial recognition and verification have been observed to have the most optimistic results. Our research focuses on the analysis of di erent Convolutional Neural Networks (CNNs) that have been developed in recent years. We carry out an extensive analysis of the differences in the performances of the VGG-19 architecture, the ResNet-50 architecture, the InceptionResNet v2 architecture and the Xception architecture while verifying images of the same or di erent identities with a large age gap on the two widely used datasets namely the MORPH-II dataset and the FG-NET dataset. Our results show that the VGG-19 model has an accuracy rate of 58.005%, InceptionResNet v2 has 44.26%, ResNet-50 has 35.26% and lastly, VGG-19 has 24.74%.