Robustness of fingerprint verification algorithms against synthetic deformations için kapak resmi
Robustness of fingerprint verification algorithms against synthetic deformations
Cantürk, Sinem, author.
Yazar Ek Girişi:
Fiziksel Tanımlama:
x, 42 leaves: charts;+ 1 computer laser optical disc.
Fingerprint recognition is one of the biometric techniques used for the identification of humans. The developments and research about fingerprint recognition to date are of great importance in advancing fingerprint recognition and verification scenarios. The fact that fingerprint recognition systems are used almost everywhere and are easily accessible is directly proportionate to a large amount of research in these areas. During the acquisition of the fingerprint, there are many environmental factors that may affect the quality of the print and eventually, its ability to be recognized. For a fingerprint recognition algorithms, it is important to handle the difficulties that arise due to those variations. The aim of the thesis is to obtain and compare the results of not only an existing feature-based fingerprint recognition techniques but a fingerprint recognition technique that uses deep learning. The main focus is on how fingerprint verification algorithms behave under the circumstances of synthetically distorted fingerprint images. After developing two different verification systems, the goal is to compare system results with and without distorted images. The results of the two methods with and without externally added deformations effect on the fingerprint image is compared. The first system has a feature-based approach comparing the images via local features on the fingerprint. In order to do this two different descriptors that are called ORB and SIFT are used. In the feature-based approach, there is also a matching part and this part is tried with two different matching algorithms that are called Brute Force Matcher and Approximate Nearest Neighbor (ANN) matcher. The second algorithm makes the decision of match or non-match by feeding the raw fingerprint images as an input to a deep neural network and comparing the feature vectors calculated by the network. This study has revealed that deep neural network approach has given more robust and faster results on both the original dataset and distorted versions of the dataset.
Yazar Ek Girişi:
Tek Biçim Eser Adı:
Thesis (Master)--İzmir Institute of Technology: Computer Engineering.

İzmir Institute of Technology: Computer Engineering--Thesis (Master).
Elektronik Erişim:
Access to Electronic Versiyon.


Materyal Türü
Demirbaş Numarası
Yer Numarası
Durumu/İade Tarihi
Tez T002135 TK7882.B56 C22 2019

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