A Unified Framework for Age-Invariant Face Recognition Using GANs and Vision Transformers

Age-invariant face recognition (AIFR) remains a persistent challenge in computer vision due to significant facial changes over time. While existing methods tackle this problem using either generative age simulation, contrastive embedding alignment, or feature disentanglement independently, they fall short in generalization and identity preservation when evaluated across datasets. This limitation highlights the need for a unified framework that can combine the strengths of these complementary strategies into a cohesive pipeline. In this work, we propose a unified deep learning framework that integrates CycleGAN-based age simulation, contrastive learning, and deep feature extraction through multiple backbone architectures including ResNet, EfficientNet, and Vision Transformers (ViT). The proposed approach uses a Siamese network with shared weights, where each branch includes a frozen pretrained backbone and an MLP projection head, trained with contrastive loss for identity similarity estimation. This framework is designed to jointly achieve image realism, representation robustness, and cross-dataset generalization. Experiments on UTKFace, MORPH, and FG-NET datasets demonstrate the effectiveness of our method in learning robust, age-invariant facial representations. We show that models in the non-unified framework achieve 98.92% and 99.14% accuracy on MORPH, but their performance drops to 53.49% and 52.89% on FG-NET under zero-shot generalization. In contrast, the proposed unified framework maintains 80.55% and 80.08% accuracy, reducing the generalization gap by over 27% and demonstrating robust cross-age performance. The results highlight the need for a unified framework to achieve robust cross-dataset generalization in age-invariant face recognition.