Chakrabarty, AmitabhaBushra, Fairooz NawarElma, Farhat LamiaKhan, Ramisa SadequeShahba, Shiana2025-09-292025-09-2920202020-10ID 16241010ID 16201058ID 16241004ID 16241008http://hdl.handle.net/10361/26804Cataloged from PDF version of thesis.Includes bibliographical references (pages 53-55).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2020.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%.68 pagesenBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.CNNConvolutional neural networksFace veri cationFace recognitionDeep learningDeep neural networksAging face recognitionHuman face recognition (Computer science).Neural networks (Computer science).Face perception.Deep learning (Machine learning).Face--Aging.Thesis