Unveiling Facial Fidelity: A Novel Approach to Synthesizing High-Quality Face Images Using Generative Adversarial Networks
In the field of computer vision, generating realistic images from sketches has garnered considerable attention due to its wide-ranging applications in art, design, and facial recognition systems. With increasing demand for more sophisticated image synthesis, advanced methodologies are essential to b...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11030461/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850217745433493504 |
|---|---|
| author | Modafar Ati Muhammad Ahmed Hassan Muhammad Usman Ghani |
| author_facet | Modafar Ati Muhammad Ahmed Hassan Muhammad Usman Ghani |
| author_sort | Modafar Ati |
| collection | DOAJ |
| description | In the field of computer vision, generating realistic images from sketches has garnered considerable attention due to its wide-ranging applications in art, design, and facial recognition systems. With increasing demand for more sophisticated image synthesis, advanced methodologies are essential to bridge the gap between sketches and high-fidelity images. This study proposes a novel methodology that comprises two key modules: the Generator and the Discriminator. The Generator is designed to transform sketches into images, while the Discriminator extracts 512-dimensional face encodings from both generated and ground truth images. Leveraging pre-trained ResNet-101, the Discriminator uses Mean Squared Error (MSE) loss to compare the face encodings, reinforcing the Generator to produce images with face encodings closely resembling the ground truth. The research utilizes two datasets: Labeled Faces in the Wild (LFW) for training the ResNet-101 CNN model for face recognition, and CelebFaces Attributes Dataset (Celeb-A) for training the sketch-to-image generator. To assess the quality of generated images, Signal-to-Noise Ratio (SNR) and Peak Signal-to-Noise Ratio (PSNR) evaluation measures are used. The SNR steadily increases over the epochs, indicating enhanced performance, and reaches a peak value of 0.334. This indicates the model’s efficacy in reducing noise and enhancing signal strength, leading to improved overall performance. The highest achieved PSNR value of 0.334 signifies an enhancement in the quality of the reconstructed signal. In particular, the proposed model exceeds previous models, with accuracies of 99.9% on the LFW dataset and 99.4% on the Celeb-A dataset, respectively. |
| format | Article |
| id | doaj-art-8bc64df2b8c8422bb7ff344a4b55a8aa |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-8bc64df2b8c8422bb7ff344a4b55a8aa2025-08-20T02:07:59ZengIEEEIEEE Access2169-35362025-01-011310348210349610.1109/ACCESS.2025.357894011030461Unveiling Facial Fidelity: A Novel Approach to Synthesizing High-Quality Face Images Using Generative Adversarial NetworksModafar Ati0https://orcid.org/0000-0001-9250-6225Muhammad Ahmed Hassan1Muhammad Usman Ghani2https://orcid.org/0000-0001-6733-2569Computer Science and Information Technology, College of Engineering, Abu Dhabi University, Abu Dhabi, United Arab EmiratesAlkhwarizmi Institute of Computer Science, University of Engineering and Technology, Lahore, Punjab, PakistanAlkhwarizmi Institute of Computer Science, University of Engineering and Technology, Lahore, Punjab, PakistanIn the field of computer vision, generating realistic images from sketches has garnered considerable attention due to its wide-ranging applications in art, design, and facial recognition systems. With increasing demand for more sophisticated image synthesis, advanced methodologies are essential to bridge the gap between sketches and high-fidelity images. This study proposes a novel methodology that comprises two key modules: the Generator and the Discriminator. The Generator is designed to transform sketches into images, while the Discriminator extracts 512-dimensional face encodings from both generated and ground truth images. Leveraging pre-trained ResNet-101, the Discriminator uses Mean Squared Error (MSE) loss to compare the face encodings, reinforcing the Generator to produce images with face encodings closely resembling the ground truth. The research utilizes two datasets: Labeled Faces in the Wild (LFW) for training the ResNet-101 CNN model for face recognition, and CelebFaces Attributes Dataset (Celeb-A) for training the sketch-to-image generator. To assess the quality of generated images, Signal-to-Noise Ratio (SNR) and Peak Signal-to-Noise Ratio (PSNR) evaluation measures are used. The SNR steadily increases over the epochs, indicating enhanced performance, and reaches a peak value of 0.334. This indicates the model’s efficacy in reducing noise and enhancing signal strength, leading to improved overall performance. The highest achieved PSNR value of 0.334 signifies an enhancement in the quality of the reconstructed signal. In particular, the proposed model exceeds previous models, with accuracies of 99.9% on the LFW dataset and 99.4% on the Celeb-A dataset, respectively.https://ieeexplore.ieee.org/document/11030461/Computer visiondeep learninggenerative adversarial networkimage generationimage processing |
| spellingShingle | Modafar Ati Muhammad Ahmed Hassan Muhammad Usman Ghani Unveiling Facial Fidelity: A Novel Approach to Synthesizing High-Quality Face Images Using Generative Adversarial Networks IEEE Access Computer vision deep learning generative adversarial network image generation image processing |
| title | Unveiling Facial Fidelity: A Novel Approach to Synthesizing High-Quality Face Images Using Generative Adversarial Networks |
| title_full | Unveiling Facial Fidelity: A Novel Approach to Synthesizing High-Quality Face Images Using Generative Adversarial Networks |
| title_fullStr | Unveiling Facial Fidelity: A Novel Approach to Synthesizing High-Quality Face Images Using Generative Adversarial Networks |
| title_full_unstemmed | Unveiling Facial Fidelity: A Novel Approach to Synthesizing High-Quality Face Images Using Generative Adversarial Networks |
| title_short | Unveiling Facial Fidelity: A Novel Approach to Synthesizing High-Quality Face Images Using Generative Adversarial Networks |
| title_sort | unveiling facial fidelity a novel approach to synthesizing high quality face images using generative adversarial networks |
| topic | Computer vision deep learning generative adversarial network image generation image processing |
| url | https://ieeexplore.ieee.org/document/11030461/ |
| work_keys_str_mv | AT modafarati unveilingfacialfidelityanovelapproachtosynthesizinghighqualityfaceimagesusinggenerativeadversarialnetworks AT muhammadahmedhassan unveilingfacialfidelityanovelapproachtosynthesizinghighqualityfaceimagesusinggenerativeadversarialnetworks AT muhammadusmanghani unveilingfacialfidelityanovelapproachtosynthesizinghighqualityfaceimagesusinggenerativeadversarialnetworks |