Age Transformation Using Deep Learning Techniques

Age transformation is a novel application of deep learning and computer vision that enables the simulation of aging and rejuvenation effects on facial images. This study presents the development of a real-time web-based system that performs facial age progression and regression using Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs). The proposed framework allows users to upload a frontal face image and view realistic transformations across three life stages—youth, middle age, and old age—while ensuring the individual’s identity is preserved. The system is implemented using Python with Streamlit for interface development and OpenCV for image preprocessing. It utilizes pre-trained models to achieve high-quality results and is optimized to perform robustly under diverse lighting conditions and ethnic backgrounds. The application has practical utility in entertainment, forensic simulations, virtual reality, and age-based biometric systems. This paper outlines the methodology, architectural design, expected outcomes, and future enhancements for the system. The results demonstrate that the proposed method produces visually realistic and identity-preserving transformations with high user interactivity and deployment feasibility