Volume 2 - Issue 2, March - April 2026

Generative Adversarial Networks (GANs) have brought a paradigm shift in the area of generative modeling, especially in the area of image generation. One of the most successful applications of GANs is the generation of images of the human face that look realistic and are indistinguishable from real images. The current project aims to apply Deep Convolutional Generative Adversarial Networks (DCGANs) to generate high-quality images of the human face. DCGANs employ the use of convolutional neural networks in both the generator and the discriminator to enhance the quality of the generated images. The model is trained on a large database of real human faces, and the result is the generation of new, realistic human faces that belong to no real person. This has a wide range of applications in the entertainment industry, gaming, art, and data augmentation in machine learning.

Nakkala Sarayu, Vakeel Samanjasa, Kura Venkatesh, Nagasani Shiva,"Cloning Human Faces Using a DCGAN Model" International Journal of Advanced Multidisciplinary Research and Educational Development, V2(2): Page(1343-1346) Mar-Apr 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.