A Novel Hybrid Age GAN Framework for Realistic Gender-Aware and Identity-Preserving Face Aging
Facial aging simulation has become an essential instrument in various fields, and some of them are forensic medicine, cosmetic surgery, entertainment, and mental health. Within the scope of criminal investigation, age progression approaches help in forecasting how missing persons might look like in the future. This paper presents the proposed Hybrid Age GAN framework, termed MP-CGAN (Multi-Phase Conditional Generative Adversarial Network), developed for generating realistic, progressive facial aging. The proposed model is built upon a conditional GAN backbone, incorporating identity-preserving representation, age- and gender-conditioned feature modulation via Adaptive Instance Normalization, residual aging blocks for gradual structural transformation, and self-attention mechanisms targeting aging-sensitive facial regions. A multi-scale discriminator operating at three resolutions (128×128, 64×64, 32×32) enforces realism at both global and fine-grained levels. In contrast to approaches that focus solely on architectural design, this research primarily addresses training stability as a fundamental factor in achieving realistic aging. A multi-phase progressive training strategy, supported by N_CRITIC update scheduling and label smoothing, was adopted to systematically resolve the discriminator collapse problem D_DEAD. The model was trained on the UTK Face dataset and used 5,500 balanced face images covering 11 age groups, including both genders, over 100 training epochs. Experimental results demonstrate strong performance with a Frechet Inception Distance (FID) of 27.78, Structural Similarity Index (SSIM) of 0.6580, Peak Signal-to-Noise Ratio (PSNR) of 19.72 dB, Cosine Identity Similarity (CSIM) of 0.7818, and Mean Absolute Error (MAE) of 7.09 years at the age bucket classification level.