Fake Human Face Generation Using DCGAN

Abstract

In recent years, Artificial Intelligence has experienced significant growth across various computer vision applications. This project focuses on designing and training a model based on an advanced class of Convolutional Neural Networks known as Deep Convolutional Generative Adversarial Networks (DCGANs), using a dataset of human faces. The primary objective is to enable the Generator network to produce new facial images that closely resemble real ones, achieving a high level of realism. This advancement has contributed notably to developments in industries such as animation and advertising. Data collection and preprocessing are often timeintensive stages in machine learning workflows. To address this challenge, Generative Adversarial Networks (GANs) are utilized, offering an efficient approach to learning from existing data and generating new samples. GANs have emerged as one of the most powerful unsupervised learning techniques in recent years, particularly for image-based tasks. This study explores the capability of GANs to synthesize realistic human face images, demonstrating their effectiveness in generating high-quality visual content.