Fingerprint image enhancement using fully convolutional deep autoencoders / Destaque de imagens de impressão digital utilizando autoencoders profundos totalmente convolucionais

Image quality for fingerprint samples is critical for the matching process. Novel methods introduce deep learning matching techniques based on convolutions neural networks to enhance degraded fingerprint images. However, due to the nature of the enhanced image problem, these methods tend to rely on processing small image patches to achieve their goal. Such an approach may often yield satisfactory results while having high computational costs due to overlapping in patches. In this paper, we propose a fast and accurate fully convolutional neural network based on an auto-encoder architecture to enhance the quality of fingerprint images. We do not use the patch processing method and instead train a model to enhance the image as a whole. After exhaustive testing, we achieve a model that can quickly perform the desired task, while achieving an average of 97.956% and 83.748% per pixel accuracy on the easiest and hardest dataset respectively. The models were trained on the publicly available Fingerprint Verification Competition datasets. We then highlight the most general model that can best enhance the quality of all datasets

Multimodal Biometrics Enhancement Recognition System based on Fusion of Fingerprint and PalmPrint: A Review

This article is an overview of a current multimodal biometrics research based on fingerprint and palm-print. It explains the pervious study for each modal separately and its fusion technique with another biometric modal. The basic biometric system consists of four stages: firstly, the sensor which is used for enrolmen

Recognition and Classification of Fingerprints

Import 03/11/2016Cílem této bakalářské práce je seznámení se s technikami zpracování digitálního obrazu otisku prstu, a následné využití těchto znalostí při realizaci softwaru pro porovnání dvou otisků prstů, tedy porovnání 1:1. Algoritmus je napsán v programovacím prostředí MATLAB. K řešení byla použita metoda, která srovnává dva otisky na základě jejich specifických rysů, což jsou markanty a singulární body. Jednotlivé operace a postupy, které byly využity při implementaci vlastního klasifikačního algoritmu, jsou podrobně rozebrány v teoretické části této práce. Kompletní softwarové řešení je popsáno v praktické části. Jsou zde taktéž uvedeny konkrétní aplikované postupy. Výsledný program je ovládaný prostřednictvím uživatelské rozhraní, umožňující nastavit jednotlivé parametry zpracování, případně zvolit operace, které budou na otisky aplikovány.The objective of this bachelor thesis is to become acquainted with different techniques of digital image processing of fingerprint, and use them to implement software for comparison of two fingerprints 1:1. The algorithm is written in MATLAB. The method of comparison of two fingerprints according to their features, singular points and minutiae, was used in the final solution. Each operation and procedure, which were used in implementation of own classification algorithm, are described in theoretical part of this work. Complete software solution is described in practical part. This part also contains all applied specific procedures. The final program is controled by an user interface, where each parameter of image processing and election of operations can be set.450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn