Published Paper Details:

In recent years, there has been significant rise in the recognition and adaption of Artificial Intelligence and Machine Learning. Now A days Face age progression is becoming a widely used technique in the modern era as it serves numerous applications. Real-time face aging progression using Generative Adversarial Networks (GANs) is an advanced deep learning approach that enables the realistic simulation of facial aging and rejuvenation. Unlike traditional image processing methods, GANs ensure photorealistic results by synthesizing fine-grained details such as wrinkles, skin texture, and facial shape variations. The proposed system can be applied in various fields, including forensics, entertainment, and digital personalization. By implementing real-time processing approach, which ensures instant and seamless age transformation, making it suitable for interactive applications. The integration of advanced deep learning techniques, such as attention mechanisms and progressive training, further enhances accuracy and realism. This study explores the potential of GAN-based face aging progression, highlighting its effectiveness in generating lifelike aging transformations while addressing challenges such as identity preservation and dataset biases. In our project, we will explore and implement face progression over different ages. We will use some frameworks, Libraries in Deep Learning Networks and Python to implement our application. Using Generative Adversarial Networks, we seek to generate older versions of oneself while preserving the identity of the individual. Using Generative Adversarial Networks, we seek to generate older versions of oneself while preserving the identity of the individual.