20.500.12556/RUL-165650 Generation of synthetic fingermarks Generiranje sintetičnih prstnih sledi Fingerprints collected from various surfaces, also known as fingermarks, are important evidence for identifying subjects that were present on a crime scene. With the recent advances in deep learning aproaches, methods have been developed to identify fignermarks and match them to subjects in police databases. However, training such a method has proven to be difficult due to a lack of training data. Such datasets are difficult and expensive to collect, and come with privacy concerns. In this work, we explore the suitability of diffusion models, which have recently gained popularity in image generation tasks, for the task of generating a dataset of varied and realistic fingermark impressions of a known identity. We finetune a latent diffusion model using low-rank adaptation and ControlNet guidance. We show that our approach is capable of generating high-quality and varied samples after being trained on only 20 images of fingermarks for each style. We also show that ControlNet can be used to guide the generation of fingermarks towards a wanted identity, despite it being trained only on synthetic images. Prstni odtisi odvzeti iz raznih površin, imenovani prstne sledi, so pomemben dokaz za identifikacijo osumljencev, ki so bili prisotni na kraju zločina. Z nedavnimi napredki v pristopih globokega učenja se je razvilo tudi več metod za razpoznavanje prstnih sledi in za njihovo ujemanje z osebam v policijskih zbirkah podatkov. Učenje takih metod je težavno zaradi pomankanja podatkov za učenje, ker je primerne zbirke podatkov težko in drago zbirati in pride s skrbmi o zasebnosti. V tem delu raziščemo primernost difuzijskih mrež, ki se v zadnjem času veliko uporabljajo za naloge generiranja slik, za generacijo raznolikih in realističnih prstnih sledi znane identitete. Naučili smo latentni difuzijski model z uporabo adaptacije nizke stopnje in ControlNet vodenjem. Pokažemo, da je naš pristop sposoben generirati prstne sledi visoke kvalitete in raznolikosti z učenjem s samo 20 slikami za vsak stil sledi. Pokažemo tudi, da ControlNet model lahko vodi generacijo slik v znano identiteto, čeprav je bil treniran samo na sintetičnih slikah. image generation diffusion model biometry fingermarks generacija slik difuzijski model biometrija prstne sledi true false false Angleški jezik Slovenski jezik Magistrsko delo/naloga 2024-12-11 12:45:02 2024-12-11 12:45:04 2024-12-12 09:15:04 0000-00-00 00:00:00 2024 0 0 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 37079 218711555 Likozar_Janus_-_Generiranje_sinteticnih_prstnih_sledi.pdf Likozar_Janus_-_Generiranje_sinteticnih_prstnih_sledi.pdf 1 041C452560C470B03A640E89B82AA93C ce544dd3122b2d8a32fcaac54f662092ad1a67735d055d172eb926b296879c0f df3172e8-b7b4-11ef-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=196879 Fakulteta za računalništvo in informatiko 0 0 0