Enhance Gender and Identity Preservation in Face Aging Simulation for Infants and Toddlers

TL;DR

This paper introduces an enhanced deep learning model for face aging simulation of infants and toddlers, improving gender accuracy and identity preservation over previous methods, with promising results on a large dataset.

Contribution

The authors extend the CAAE model to include gender information and an identity-preserving component, significantly improving aging simulation quality for young faces.

Findings

77.0% improvement in gender fidelity for males

13.8% improvement in gender fidelity for females

22.4% enhancement in identity preservation

Abstract

Realistic age-progressed photos provide invaluable biometric information in a wide range of applications. In recent years, deep learning-based approaches have made remarkable progress in modeling the aging process of the human face. Nevertheless, it remains a challenging task to generate accurate age-progressed faces from infant or toddler photos. In particular, the lack of visually detectable gender characteristics and the drastic appearance changes in early life contribute to the difficulty of the task. We propose a new deep learning method inspired by the successful Conditional Adversarial Autoencoder (CAAE, 2017) model. In our approach, we extend the CAAE architecture to 1) incorporate gender information, and 2) augment the model's overall architecture with an identity-preserving component based on facial features. We trained our model using the publicly available UTKFace dataset and evaluated our model by simulating up to 100 years of aging on 1,156 male and 1,207 female infant and toddler face photos. Compared to the CAAE approach, our new model demonstrates noticeable visual improvements. Quantitatively, our model exhibits an overall gain of 77.0% (male) and 13.8% (female) in gender fidelity measured by a gender classifier for the simulated photos across the age spectrum. Our model also demonstrates a 22.4% gain in identity preservation measured by a facial recognition neural network.