Enhanced Deep Convolutional Generative Adversarial Network for High-Fidelity Human Face Synthesis

Abstract

Generative Adversarial Networks (GANs) are a potent tool for generating photorealistic synthetic images. In this study, we aim to build and test a Deep Convolutional Generative Adversarial Network (DCGAN) that can produce realistic portraits of people's faces. One network learns to transform random noise vectors into realistic face photographs, while the other learns to differentiate between real and fraudulent photos; both networks are trained on the CelebA dataset. The proposed design uses convolutional layers in the Discriminator and transposed convolutional (deconvolutional) layers in the Generator to improve image quality and training stability. Binary cross-entropy loss, the Adam optimizer, and suitable data normalization are some of the strategies used to ensure effective learning and better convergence. The results prove that DCGANs are capable of creating realistic facial representations. To evaluate the model's efficacy, we use both qualitative and quantitative measures, such as Frechet Inception Distance (FID). FID measures how similar the distributions of the real and generated pictures are to one another. The results of this study demonstrate that DCGANs are very promising for applications in the entertainment, virtual reality, data augmentation, and AI-powered creative content production industries.