GFAM: A Gender-Preserving Face Aging Model for Age Imbalance Data

Abstract

The objective of face aging is to generate facial images that present the effects of aging. The existing one-hot encoding method for aging and/or rejuvenation patterns overlooks the personalized patterns for different genders and races, causing errors such as a male beard appearing on an aged female face. A gender-preserving face aging model is proposed to address these issues, termed GFAM. GFAM employs a generative adversarial network and includes several subnetworks that simulate the aging process between two adjacent age groups to learn specific aging effects. Specifically, the proposed model introduces a gender classifier and gender loss function that uses gender information as a self-guiding mechanism for maintaining gender attributes. To maintain the identity information of synthetic faces, the proposed model also introduces an identity-preserving module. Additionally, age balance loss is used to mitigate the impact of imbalanced age distribution and enhance the accuracy of aging predictions. Moreover, we construct a dataset with balanced age distribution for the task of face age progression, referred to as Age_FR. This dataset is expected to facilitate current research efforts. Ablation studies have been conducted to extensively evaluate the performance improvements achieved by our method. We obtained relative improvements of 3.75% higher than the model without the gender preserving module. The experimental results provide evidence of the effectiveness of the proposed method, both through qualitative and quantitative analyses. Notably, the mean face verification accuracy for the age-progressed groups (0–20, 31–40, 41–50, and 51–60) was found to be 100%, 99.83%, 99.79%, and 99.11%, respectively, highlighting the robustness of our approach across various age ranges.