Realistic Face Super-Resolution via Generative Adversarial Networks: Enhancing Facial Recognition in Real-world Scenarios
Journal Title: International Journal of Innovations in Science and Technology - Year 2025, Vol 7, Issue 2
Abstract
The accuracy of real-world facial recognition operations faces challenges because of the difficulties connected to Low-Resolution image quality. Thisindicates that super-resolution methods play a vital role inimproving recognition outcomes. Currently,available SR techniques do not achieve generalization due to their dependence on synthetic LR data that uses basic down samplingprocesses. The proposed GAN-based approach establishes a solution to this challenge through its simulation of actual degradation algorithms which combine Gaussian blur with noise addition and color modification and JPEG compression. Random application of augmentation parameters allows the GAN model to acquire knowledge about diverse and realistic low-resolution data distribution patterns during training. A unique unaligned face image pair dataset was made specifically for research using Zoom-In and Zoom-Out methods to capture high-resolution and low-resolution images from the same individuals. The dataset presents authentic real-life scenarios better than conventional paired collection methods. Based on experimental results our method produces substantial gains in performance compared to other super-resolution methods across both self-created face data as well as established surveillance data. The proposed model achieves higher visual quality standards while improving facial recognition accuracy under different operational situations. In conclusion, this study implements an effective SR solution for facial recognition which tackles problems with standard training datasets while creating authentic face image data. The proposed method shows promise for enhancing SR applications which need high-quality facial recognition capability in surveillance systems and other security-based operations.
Authors and Affiliations
Muhammad Nouman Ashraf, Muhammad Farooq, Muhammad Shahid Farid, Muhammad Hassan Khan, Shuja-ur-RehmanBaig
Keywords
Related Articles
- EP ID EP765289
- DOI -
- Views 17
- Downloads 0
Impact of COVID-19 Lockdown Measures on Air Quality in Khyber Pakhtunkhwa Province: A Google Earth Engine Based Study
Air pollution poses a critical challenge to urban sustainability and public health. The COVID-19 lockdown created a unique opportunity to study the effects of reduced human and industrial activities on air quality. In...
Python-Based Land Suitability Analysis for Wheat Cultivation Using MCEand Google Earth Engine in Punjab-Pakistan
The present study aims to examine the suitability of wheat crops in the four districts of Sheikhupura, Gujranwala, Hafizabad, and Nankana Sahib by conducting a thorough examination of various environmental parameters....
Performance Analysis ofMotorbike Engine Using Bioethanol Gasoline Blends
The increasing demand for sustainable energy and reduced reliance on fossil fuels has driven the exploration of alternative fuel options. This study aims to evaluate the performance of a motorcycle engine using bioetha...
Leveraging CIELAB Segmentation and CNN for Wheat Fungi Disease Classification
Wheat is the third most harvested and consumed grain globally, but a significant portion of its production is wasted due to diseases. Fungal infections caused by pathogenic fungi are particularly harmful, greatly reduc...
Exploring Contextual Similarity in Quranic Ayahs: A Case Study of Surah Al-Baqarah and Aal-e-Imran in Urdu Translations
The translation of sacred texts, particularly the Quran, requires a deep understanding of both linguistic and contextual nuances to preserve the original message. This research investigates the contextual similarity am...