# The Effects of AI-Driven Face Restoration on Forensic Face Recognition
###### Abstract  
In biometric recognition, face recognition is a mature and widely used technique that provides a fast, accurate, and reliable method for human identification. This paper aims to study the effects of face image restoration for forensic face recognition and then further analyzes the advantages and limitations of the four state-of-the-art face image restoration methods in the field of face recognition for forensic human image identification. In total, 100 face image materials from an open-source face image dataset are used for experiments. The Gaussian blur processing is applied to simulate the effect of blurred face images in actual cases of forensic human image identification. Four state-of-the-art AI-driven face restoration methods are used to restore the blurred face images. We use three mainstream face recognition systems to evaluate the recognition performance changes of the blurred face images and the restored face images. We find that although face image restoration can effectively remove facial noise and blurring effects, the restored images do not significantly improve the recognition performance of the face recognition systems. Face image restoration may change the original features in face images and introduce new made-up image features, thereby affecting the accuracy of face recognition. In current conditions, the improvement in face image restoration on the recognition performance of face recognition systems is limited, but it still has a positive role in the application of forensic human image identification. face restoration; face recognition; face recognition system; forensic human image identification; face image +
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remained lower than that achieved by AFRS alone [11]. Therefore, it is of great significance to effectively solve the above problems and improve the accuracy and reliability of face recognition and forensic human image identification for achieving more secure and reliable identity recognition applications. Face image restoration methods can effectively solve the above problems. Image restoration refers to the use of computer algorithms and techniques to repair and restore defects, noise, stains, and other issues in images, making the images clearer, more beautiful, and complete [12; 13; 14]. The latest guidelines for image quality restoration include that facial details/textures should be visible and the face should be fully visible within the image [15]. Common image restoration techniques include contrast enhancement, denoising, inpainting, restoration, and reconstruction [16; 17]. These techniques can be applied in various fields, such as digital image processing, medical image processing, and forensic identification. Unlike traditional image processing algorithms used in the existing literature, such as non-local central sparse representation [18] and natural image block models [19], in this paper, we use emerging AI-driven face restoration methods for study. The AI-driven face restoration significantly differs from traditional face enhancement algorithms in terms of appearance restoration effects. AI-driven face restoration technology refers to the use of artificial intelligence technology to repair missing or damaged facial components through computer vision algorithms [20]. By processing low-quality images, AI-driven image restoration techniques can significantly improve image quality with good results for facial details and textures in the restored images. AI-driven image restoration techniques mainly include denoising, deblurring, super-resolution, and image inpainting [21; 22], which eliminate the impact of deteriorating image quality by preprocessing and postprocessing, thereby enhancing the identity recognizability of facial appearance.