Generating Photorealistic Facial Composites with Diffusion ModelsMiočić, Matej (Avtor) Peer, Peter (Mentor) Tomašević, Darian (Komentor) Computer VisionDeep LearningImage-Based BiometricsDiffusion ModelsFacial CompositeFacial composites, also known as police sketches, are essential tools in law enforcement for reconstructing a suspect’s appearance based on eye witness descriptions. Traditionally, forensic artists manually create these sketches by working closely with eyewitnesses, which is often slow and te dious. To simplify this process, modern law enforcement increasingly relies on advanced computer software. While deep learning has introduced several innovative approaches in recent years, the use of diffusion models for this task remains largely unexplored. We present Diff-FIT (Diffusion Facial Identification Technique), a novel framework for generating photorealistic facial composites using diffusion models. Diff-FIT enables fast generation of initial images from a textual description, followed by intuitive, sequen tial edits based on ongoing eyewitness input. Our approach generates facial composites with a preference comparable to existing methods, while enabling a greater range of facial variation and more diverse adjustments, without compromising identification performance or image quality. In a user study involving biometric experts and non-experts, facial composites generated by Diff-FIT were rated on par with those from state-of-the-art methods in both subjective evaluations and identification rates.20252025-09-02 13:00:00Magistrsko delo/naloga171779VisID: 37773COBISS_ID: 248392963sl