  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.
