PReFace : AI -Assisted Plausible Facial Restoration and Damage Reconstruction System

Computer Science and Engineering (Computer Vision, Deep Learning and Image Processing)

Facial injury can obscure visually important structures, creating a challenging image reconstruction problem in which the original appearance is unknown. This paper presents the proposed PreFace architecture: a single-image, AI-assisted workflow combining RetinaFace for face localisation, U-Net for pixel-level damage segmentation, and LaMa for mask-guided image inpainting. The design includes synthetic training pairs derived from approximately 30,000 appropriately licensed clean facial images, four proposed damage-severity categories, and a Streamlit interface with General and Forensic user roles. The Forensic role adds a downloadable PDF analysis report; it does not establish the authenticity of model-generated features. We specify preprocessing, mask generation, restoration, application integration, evaluation metrics and failure-case testing. The quotation defines expected deliverables but supplies no completed checkpoints, measured reconstruction scores or real-world validation data. Consequently, this paper contributes an engineering design and reproducible evaluation protocol rather than unverified performance claims. The generated portrait is a plausible model prediction, not a proven reconstruction of the subject’s pre-injury face. The proposed evaluation protocol reports segmentation through Dice score, intersection-over-union, precision and recall, and reconstruction through PSNR, SSIM and LPIPS, stratified by the four severity levels and by missing-feature cases, with inference time and failure cases documented. The paper also reviews face-restoration and inpainting research from 2020 to 2026, specifies a dataset partition of 20,000 training, 5,000 validation and 5,000 test images with identity-aware separation, and discusses privacy, ethical use and the limits of forensic interpretation. All numeric results are deliberately left unreported until trained checkpoints and validated test logs exist; future work will audit licensed data, train the models and test generalisation to real-world images.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.