Analysis of fingerprint matching performance with deep neural networks için kapak resmi
Analysis of fingerprint matching performance with deep neural networks
Göçen, Alper, author.
Yazar Ek Girişi:
Fiziksel Tanımlama:
vii, 35 leaves: charts;+ 1 computer laser optical disc.
Fingerprints are unique biometric properties for each person. In the literature and industry, they are widely used for identification purposes. Collecting biometric datasets is a tedious work since it is not possible without the owners’ consent, and existing fingerprint datasets are either not sufficient to use in deep learning tasks by means of size or most of them are kept private to the collectors’ use. This increases the need of synthetic fingerprint images and their use in a variety of tasks especially for training deep learning models. In this study, the performance of a CNN architecture named Finger ConvNet[1] is compared to well-known networks and the question of whether a mixed dataset consisting of synthetically generated and real fingerprint images can reach a performance close or equal to ones having only real images is discussed. As a result of experiments, it is shown that the number of real images in the dataset is an important factor and that the performance of the mixed dataset was less than the one having only real images proposed in the referred study[1].
Yazar Ek Girişi:
Tek Biçim Eser Adı:
Thesis (Master)--İzmir Institute of Technology:Computer Engineering.

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


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Durumu/İade Tarihi
Tez T002472 QA76.87 .G576 2022

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