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.