<![CDATA[This study investigates the capability of the Generative Adversarial Network (GAN) algorithm, specifically GFPGAN (Generative Face Prior-Generative Adversarial Network), in enhancing the quality of facial images to support more accurate face recognition. GFPGAN is known for its effectiveness in restoring degraded facial images, such as those affected by blurriness, noise, and low resolution—common issues in CCTV (Closed-Circuit Television) footage or other low-quality image sources. By leveraging the GAN architecture, which consists of a generator and a discriminator, GFPGAN is able to produce high-detail facial images while preserving the original facial identity. In this research, GFPGAN is utilized to restore degraded facial images prior to the face recognition process using the DLIB library. Various types of image degradation are tested, including blur, noise, grayscale conversion, and JPEG (Joint Photographic Experts Group) compression. The evaluation involves comparing the face recognition success rate using DLIB before and after the restoration process with GFPGAN. The results demonstrate that previously unrecognizable images become identifiable after being processed with GFPGAN, thereby confirming that image restoration can significantly improve face recognition accuracy.]]>
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