Novel approach to Create Human Faces with DCGAN for Face Recognition


Roshni Khedgaonkar
Kavita Singh
Sunny Mate


Due to the remarkable data generation abilities of the generative models, many generative adversarial networks (GAN) models have been developed, and several real-world applications in computer vision and machine learning have emerged. The generative models have received significant attention in the field of unsupervised learning via this new and useful framework. In spite of GAN's outstanding performance, steady training remains a challenge. In this model, use of Deep Convolutional Generative Adversarial Networks is incorporated, Main aim is to produce human faces from unlabeled data. Face generation has a wide range of applications in image processing, entertainment, and other industries. Extensive simulation is performed on the CelebA     dataset. Key focus is to successfully construct human faces from the unlabeled data and random noise and achieved average loss of 1.115% and 0.5894 % for generator and discriminator respectively.


How to Cite
Roshni Khedgaonkar, Kavita Singh, & Sunny Mate. (2022). Novel approach to Create Human Faces with DCGAN for Face Recognition . International Journal of Next-Generation Computing, 13(5).


  1. Aslan, S., Gud¨ ukbay, U. ¨ , Toreyin, B. U. ¨ , and Cetin, A. E. 2019. Deep convolutional generative adversarial networks based flame detection in video. arXiv preprint arXiv:1902.01824 .
  2. Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., and Bharath, A. A. 2018. Generative adversarial networks: An overview. IEEE signal processing magazine 35, 1, 53–65.
  3. Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. 2009. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition. Ieee, 248–255.
  4. Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. 2014. Generative adversarial networks.
  5. Kingma, D. P. and Ba, J. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 .
  6. Radford, A., Metz, L., and Chintala, S. 2015. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434
  7. Wang, Z., She, Q., and Ward, T. E. 2021. Generative adversarial networks in computer vision: A survey and taxonomy. ACM Computing Surveys (CSUR) 54, 2, 1–38.
  8. Yu, F., Seff, A., Zhang, Y., Song, S., Funkhouser, T., and Xiao, J. 2015. Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop. arXiv preprint arXiv:1506.03365
  9. Zeiler, M. D. and Fergus, R. 2014. Visualizing and understanding convolutional networks. In European conference on computer vision. Springer, 818–833