Forensics of Low-Quality Facial Images from CCTV Using The Generative Adversarial Network (GAN) Method - Muhammad Adil Kustian; Ahmad Luthfi (Mobile and Forensics, 2026)
CCTV facial images often suffer from poor quality due to low resolution, motion blur, and poor illumination, complicating forensic investigation and identification. This situation necessitates more modern image restoration methods. This study proposes a Generative Adversarial Network (GAN)-based pipeline that combines two architectures, Real- ESRGAN for resolution enhancement and GFPGAN for more natural facial feature recovery. Experimental results show significant improvements in perceptual quality with a decrease in NIQE values from 12.56 to 7.81 and BRISQUE from 70.81 to 44.23, with an 82% image recovery success rate. Additional evaluations using texture entropy and gradient histograms demonstrate consistency in facial structure and edge sharpness. This study contributes by demonstrating an integrated two- stage GAN approach as an effective solution for face recovery in low-quality CCTV images, while highlighting the need for standardized forensic protocols and facial identity validation in real-world applications. Thus, this pipeline can serve as a pre-processing stage to improve image readability and has the potential to be used as a tool for forensic investigations.