Learning Face Age Progression: A Pyramid Architecture of GANs

The two underlying requirements of face age progression, i.e. aging accuracy and identity permanence, are not well studied in the literature. In this paper, we present a novel generative adversarial network based approach. It separately models the constraints for the intrinsic subject-specific characteristics and the age-specific facial changes with respect to the elapsed time, ensuring that the generated faces present desired aging effects while simultaneously keeping personalized properties stable. Further, to generate more lifelike facial details, high-level age-specific features conveyed by the synthesized face are estimated by a pyramidal adversarial discriminator at multiple scales, which simulates the aging effects in a finer manner. The proposed method is applicable to diverse face samples in the presence of variations in pose, expression, makeup, etc., and remarkably vivid aging effects are achieved. Both visual fidelity and quantitative evaluations show that the approach advances the state-of-the-art.Comment: CVPR 2018. V4 and V2 are the same, i.e. the conference version; V3 is a related but different work, which is mistakenly submitted and will be submitted as a new arXiv pape

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

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%.Comment: 3 pages, 2 figure