Enhance Gender and Identity Preservation in Face Aging Simulation for Infants and Toddlers
This work addresses a challenging domain-specific problem in biometrics for applications like missing child identification, but it is incremental as it builds on an existing deep learning model.
The paper tackled the problem of generating accurate age-progressed faces from infant or toddler photos by enhancing gender and identity preservation, resulting in a model that showed a 77.0% gain in gender fidelity for males, 13.8% for females, and a 22.4% gain in identity preservation compared to a baseline.
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.