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Local Histogram Analysis in Fingerprint Images
Glesner, Ladislav ; Drahanský, Martin (referee) ; Kanich, Ondřej (advisor)
This bachelor thesis deals with local histograms. Using these histograms, the fingerprint quality at a given location is determined. Analysis of local histograms is done using my proposed algorithms. The resulting algorithms were tested on the fingerprint database and on the damaged fingerprint database. Using these tests, a database of local histograms with a fingerprint quality rating at a specific location is created. As an extension, an algorithm for determining the quality of the entire fingerprint image is implemented.
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Generation of Skin Disease Effects into Synthetic Fingerprints from Anguli Generator
Hytychová, Tereza ; Drahanský, Martin (referee) ; Kanich, Ondřej (advisor)
The aim of this bachelor thesis is to design and implement a tool for generation of skin disease effects into synthetic fingerprints from Anguli generator. The proposed algorithms are capable of creating images with effects of warts and hyperkeratotic eczema. The OpenCV library was used for image processing. The resulting images are tested by VeriFinger and can be used for testing fingerprint recognition systems. Test results proved that both of the diseases have negative impact on fingerprint recognition. By adding effects of warts to a~fingeprint image, the image quality has decreased by up to 34 % and by adding effects of hypekeratotic eczema, the quality has decreased by up to 77 %.
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Generation of Skin Disease into the Synthetic Fingerprints
Bárta, Milan ; Kanich, Ondřej (referee) ; Drahanský, Martin (advisor)
The thesis deals with design and implementation of a tool for simulating marks of chosen skin diseases into a synthetic fingerprint. The diseases selected to work with are warts and atopic eczema. The marks of diseases are generated into a synthetic fingerprint image created by the SFinGe application. Existing disesase-affected fingerprints from the STRaDe database are analysed in detail. Then, methods for simulating the diseases into a synthetic fingerprint are proposed, implemented, and the results are evaluated.
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Analysis of Convolutional Neural Networks for Detection and Classification of Damages in Fingerprint Images
Fořtová, Kateřina ; Tinka, Jan (referee) ; Kanich, Ondřej (advisor)
The aim of this Master's thesis is to analyze detection and classification approaches using convolutional neural networks on the problem of fingerprint damage. The first part of the thesis deals with the study of literature related to biometrics and fingerprint processing with emphasis on possible diseases that may affect the fingertip area. Subsequently, the thesis focuses on neural network-based recognition. The thesis describes the architectures of convolutional neural networks and object detection approaches up to the latest research. Several detection methods for detection and classification of skin diseases affecting fingertip are proposed using modern architectures, different types of backbone networks and detection methods. Eight models based on four different detection and classification approaches are chosen for the experiments. Subsequently, each model is trained several times using configuration parameter adjustments. The models are assessed on the basis of various metrics and compared in terms of the use of the backbone network and the chosen method for detection. The best result of 76.875 % was achieved in the test of correctly detected and classified area on real fingerprint images. The most problematic disease for detection and classification was atopic eczema, whose symptoms can manifest in many ways.
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