# The Effects of AI-Driven Face Restoration on Forensic Face Recognition
###### Abstract
For example, denoising techniques can eliminate noise in images, making facial features clearer [23; 24; 25]; deblurring techniques can improve image blurring caused by motion and focus; super-resolution techniques can increase image resolution, making facial features more delicate [26; 27; 28]; image inpainting techniques can restore obscured or damaged areas. Among these techniques, deep learning has become the core method of image restoration. In particular, convolutional neural networks (CNNs) and generative adversarial networks (GANs) perform particularly well in image restoration tasks [29; 30; 31]. CNNs can automatically learn the hierarchy and semantic information in images through convolutional layers with multiple layers, while GANs can generate restoration results close to reality through adversarial learning between generators and discriminators [32; 33; 34; 35; 36]. In addition, advanced deep learning techniques such as attention mechanisms and recurrent neural networks (RNNs) [32; 37; 38] have also been widely used in the field of image restoration. Currently, many AI-driven image restoration techniques have achieved significant results in the fields of face recognition and human identification. These methods not only provide a theoretical basis for exploring the quality of face images (the performance of face recognition systems on low-quality images) but also provide stronger technical support for human identification. This paper studies the effects of state-of-the-art face image restoration techniques on face recognition performance in face recognition systems and analyzes the advantages and limitations of face image restoration methods in the field of forensic human image identification. This study selects 100 face images from open-source face image datasets and simulates blurred face image quality in actual human identification cases using Gaussian blur processing. Four different AI-driven face restoration methods, which are currently leading in high performance, are used to restore the blurred face images, and the similarity quantization values of the restored face images and the original face images are calculated and compared with the blurred face images using the face recognition system to evaluate the specific quantitative impact of the restored face image quality on the recognition performance of the face recognition system. In addition, this study will use different mainstream recognition systems to conduct experiments to explore the differences in recognition effects of restored face images in different recognition systems, aiming to comprehensively understand the relationship between face image restoration and face recognition systems in the field of human identification. The main contributions are summarized as follows:  
1. Improve image quality with AI-powered image repair technology in the field of forensic human image identification. 2. The quantitative effect of the quality of the repaired face image on the recognition performance of the recognition system is analyzed. 3. To explore the difference in recognition effect of repaired face images in different recognition systems.