  Automated face recognition and identification softwares are becoming part of
our daily life; it finds 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 effects of aging come into
the picture. Little work exists in the literature on the subject of face
prediction that accounts for aging, 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 efficient. 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 Artificial 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%.
