Developing a Face Aging Model Using Face Synthesis Techniques

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Regarding the realm of advancing technology, the subsequent development of face aging applications has garnered substantial interest. This research examines face synthesis techniques, with specific emphasis for the various practical usage of computational adversarial networks (GANs). The key objective of our project is to upgrade both the discriminator and the generator parts of GANs to generate more realistic, age-progressed face images. The project assimilates Python programming, convolutional neural networks (CNNs), and several other machine learning and image processing proceeds, advancing in qualitative data from Kaggle. The goal of our project is to advancement of face-aging technology over the use of this multifaceted method, with the possible uses in amusement, forensic science, and technological process.