Automated face recognition and identi cation softwares are becoming part of
our daily life; it nds its abode not only with Facebook's auto photo tagging, Apple's iPhoto, Google's Picasa, Microsoft's Kinect, but also in Homeland Security Department's dedicated biometric face detection systems. Most of these automatic face identification systems fail where the e ects of aging come into the picture. Little work exists in the literature on the subject of face prediction that accounts for ageing, which is a vital part of the computer face recognition systems. In recent years, individual face components' (e.g. eyes, nose, mouth) features based matching algorithms have emerged, but these approaches are still not e cient. Therefore, in this work we describe a Face Prediction Model (FPM), which predicts human face aging or growth related image variation using Principle Component Analysis (PCA) and Arti cial Neural Network (ANN) learning techniques. The FPM captures the
facial changes, which occur with human aging and predicts the facial image with a few years of gap with an acceptable accuracy of face matching from 76 to 86%.