Generation of Human Faces Using Generative Adversarial Network

Supritha P O; Prajwal; Saikumar Laxman Pujari; Siddartha R; Adarsh Shendage

doi:10.55524/ijircst.2026.14.1.4

Abstract

GANs have proven to be a powerful deep-learning framework for creating realistic synthetic images, finding wide use across various tasks in computer vision. In this work, we introduce a GAN-driven method for the generation of human face images using a deep convolutional structure. We trained our model on 9,090 resized facial images to 128 × 128 pixels from the without-mask portion of the Face Mask Lite Dataset. In this adversarial setup, the Generator synthesizes facial images from random noise vectors, while the Discriminator distinguishes between real and generated samples. The main goal here is to build a lightweight, computation-friendly GAN that still yields visually convincing face images without resorting to heavy architectures. These experiments show that the model captures the essential facial features-symmetry, texture, and overall appearance-and generates a diverse set of synthetic faces. The evaluation combines the qualitative visual inspection of generated samples with quantitative analysis of Generator and Discriminator loss trends. The results point toward stable training, realistic face generation, and a preference for architectural simplicity.

Keywords

Generative Adversarial Network, Fake Human Faces, Deep Learning, Generator, Discriminator

References

S. Singh, A. Sharma, and R. Gupta, “GAN-based human face generation using CelebA dataset,” International Journal of Computer Applications, vol. 174, no. 20, pp. 1–6, 2021.
M. Mahiuddin and A. Chowdary, “Synthetic face generation for law-enforcement applications,” IEEE Access, vol. 8, pp. 123456–123465, 2020.
V. S. K. Katta, P. S. Reddy, and K. R. Rao, “High-resolution face synthesis using DCGAN and ESRGAN,” International Journal of Emerging Technologies in Engineering Research, vol. 8, no. 6, pp. 45–50, 2020.
Wang, Y. Liu, and J. Zhang, “A comprehensive review of GAN-generated face detection methods,” IEEE Transactions on Biometrics, Behavior, and Identity Science, vol. 4, no. 2, pp. 1–15, 2022. Available from: https://ieeexplore.ieee.org/abstract/document/10604674
R. Reddy and M. Raman, “Conditional GANs for attribute-controlled face generation,” International Journal of Computer Vision and Image Processing, vol. 10, no. 3, pp. 22–35, 2020.
Goodfellow et al., “Generative adversarial networks,” in Advances in Neural Information Processing Systems (NeurIPS), pp. 2672–2680, 2014. Available from: https://tinyurl.com/bdzj9f24
Radford, L. Metz, and S. Chintala, “Unsupervised representation learning with deep convolutional GANs,” in Proc. Int. Conf. Learn. Representations (ICLR), 2016. Available from: https://doi.org/10.48550/arXiv.1511.06434
M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein GAN,” in Proc. Int. Conf. Mach. Learn. (ICML), pp. 214–223, 2017. Available from: https://proceedings.mlr.press/v70/arjovsky17a.html
Gulrajani et al., “Improved training of Wasserstein GANs,” in Advances in Neural Information Processing Systems (NeurIPS), pp. 5767–5777, 2017. Available from: https://tinyurl.com/2ppzn37x
T. Miyato et al., “Spectral normalization for generative adversarial networks,” in Proc. Int. Conf. Learn. Representations (ICLR), 2018. Available from: https://doi.org/10.48550/arXiv.1802.05957
T. Karras et al., “Progressive growing of GANs for improved quality, stability, and variation,” in Proc. Int. Conf. Learn. Representations (ICLR), 2018. Available from: https://doi.org/10.48550/arXiv.1710.10196
T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 4401–4410, 2019. Available from: https://tinyurl.com/3tz5p7r9
T. Karras et al., “Analyzing and improving the image quality of StyleGAN,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2020. Available from: https://tinyurl.com/2s4accnp
P. Isola et al., “Image-to-image translation with conditional adversarial networks,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 1125–1134, 2017. Available from: https://tinyurl.com/yupd6858
J. Zhu et al., “Unpaired image-to-image translation using CycleGAN,” in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), pp. 2223–2232, 2017. Available from: https://tinyurl.com/yc4xzedv
T. Karras et al., “Training generative adversarial networks with limited data,” in Advances in Neural Information Processing Systems (NeurIPS), 2020. Available from: https://tinyurl.com/fuva2t4k
Y. Choi et al., “StarGAN: Unified generative adversarial networks for multi-domain image translation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 8789–8797, 2018. Available from: https://ieeexplore.ieee.org/abstract/document/8545169
T. Karras et al., “Alias-free generative adversarial networks,” in Advances in Neural Information Processing Systems (NeurIPS), 2021. Available from: https://tinyurl.com/yfu6bhdu
Z. He et al., “AttGAN: Facial attribute editing by only changing what you want,” IEEE Transactions on Image Processing, vol. 28, no. 11, pp. 5464–5478, 2019. Available from: https://ieeexplore.ieee.org/abstract/document/8718508
Härkönen et al., “GANSpace: Discovering interpretable GAN controls,” in Advances in Neural Information Processing Systems (NeurIPS), 2020. Available from: https://tinyurl.com/btchhyt9

Cites this article as

S. P. O, Prajwal, S. L. Pujari, S. R, A. Shendage, "Generation of Human Faces Using Generative Adversarial Network", International Journal of Innovative Research in Computer Science and Technology (IJIRCST), Vol-14, Issue-1, Page No-25-33, 2026. Available from: https://doi.org/10.55524/ijircst.2026.14.1.4

Corresponding Author

Supritha P O

Assistant Professor, Department of Computer Science & Engineering, Sri Dharmasthala Manjunatheshwara Institute of Technology, Ujire, Karnataka, India

Download Full Paper

Download PDF

No. of Downloads: 13 | No. of Views: 206

Handwritten Notes Digitization and Multilingual Summarization Using OCR and Generative AI

Suchetha N V, Anisha Upadhayaya H S, Anushree U Rao, H N Swati, Mallana Gowda G S.

January 2026 - Vol 14, Issue 1
AgriPulse: Smart Grain Storage System

Dhawal Jain, Omkar S, Pragathi, Sanidhya M Jain, Vishal Raju Angadi.

January 2026 - Vol 14, Issue 1
Sign Language Recognition Using Deep Learning: A Systematic Review of Models and Approaches

Sruthi SJ, Evaniya Anna Suvi, Rejeena J Jajin, Annfino Jagan, Shakhy PS.

January 2026 - Vol 14, Issue 1

Generation of Human Faces Using Generative Adversarial Network

Citations

Download Full Paper PDF

Total View 206

Total Download 13