Identity Verification Using Age Progression & Machine Learning

Abstract

Facial age progression is the process of synthesizing a face image at an older age based on images showing a person at a younger age. The ability to generate accurate age progressed face images is important for a number of forensic investigation tasks. In this paper we analyze the performance of a number of publicly available age progression applications, with respect to different parameters encountered in age progression including imaging conditions of input images, presence of occluding structures, age of input/target faces, and age progression range. Through the examination and measurement of old enough movement precision within the sight of various conditions, we remove various ends that appear as a bunch of rules identified with factors that criminological craftsmen and age movement scientists should concentrate to deliver improved age movement techniques. Trial results utilizing an information base consisting of sets of face pictures that were recovered from the FGNET dataset. The check framework for faces isolated by as many AI calculations, accomplishes an equivalent exactness pace of 80%.