Face Prediction Model for an Automatic Age-invariant Face Recognition System

TL;DR

This paper introduces a Face Prediction Model that uses PCA and ANN to predict aging-related facial changes, improving face recognition accuracy across age gaps.

Contribution

The work presents a novel face prediction model combining PCA and ANN to account for aging effects in face recognition systems.

Findings

Achieves face matching accuracy of 76% to 86%.

Effectively predicts facial changes over several years.

Addresses aging challenges in automatic face recognition.

Abstract

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%.