Version 1
: Received: 2 April 2024 / Approved: 2 April 2024 / Online: 8 April 2024 (16:59:15 CEST)
How to cite:
ALI, I.M.; Jadoon, W.; Kim, S.K. Synthetic Image Generation using Conditional GAN Provided Single Sample Face Image. Preprints2024, 2024040589. https://doi.org/10.20944/preprints202404.0589.v1
ALI, I.M.; Jadoon, W.; Kim, S.K. Synthetic Image Generation using Conditional GAN Provided Single Sample Face Image. Preprints 2024, 2024040589. https://doi.org/10.20944/preprints202404.0589.v1
ALI, I.M.; Jadoon, W.; Kim, S.K. Synthetic Image Generation using Conditional GAN Provided Single Sample Face Image. Preprints2024, 2024040589. https://doi.org/10.20944/preprints202404.0589.v1
APA Style
ALI, I.M., Jadoon, W., & Kim, S.K. (2024). Synthetic Image Generation using Conditional GAN Provided Single Sample Face Image. Preprints. https://doi.org/10.20944/preprints202404.0589.v1
Chicago/Turabian Style
ALI, I.M., Waqas Jadoon and Soo Kyun Kim. 2024 "Synthetic Image Generation using Conditional GAN Provided Single Sample Face Image" Preprints. https://doi.org/10.20944/preprints202404.0589.v1
Abstract
Facial recognition systems are widely used systems around the globe. Their applications in real world scenarios like law enforcement, surveillance, ID card identification, information security, access control and many more has made them very important in today’s world. These systems have shown promising results and excellent performance; however, these systems need tremendous number of facial images for training before they are implemented in the real-world scenarios. The problem arises when we face lack of training images due to which the performance of such systems decreases exponentially. The problem becomes even worse when there is only one image per subject for training our facial recognition system, the probe image might contain variation like illumination, expression and disguise which are hard to be accurately recognized. Generative models have achieved great success for handling the lack of training data by generating synthetic data that resembles the original data. We have created a new method by fine tuning the basic Conditional Generative Adversarial Network (CGAN) to generate synthetic facial images provided only single image per subject. These synthetic facial images contain six variations that are smile, anger, stare, illumination and disguise. We increased the size of the dataset and then applied a convolutional neural network for facial recognition, which improved the accuracy of our system from 0.76 to 0.99.
Keywords
Convolutional Neural Network (CNN); Single Sample Per Person (SSPP); Conditional Generative Adversarial Network (CGAN)
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
