On the analysis of factors influencing the performance of facial age progression

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 analysis and quantification of age progression accuracy in the presence of different conditions, we extract a number of conclusions that take the form of a set of guidelines related to factors that forensic artists and age progression researchers should focus their attention in order to produce improved age progression methodologies.