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Detection and Classification of Damage in Fingerprint Images Using Neural Nets
Vican, Peter ; Rydlo, Štěpán (referee) ; Kanich, Ondřej (advisor)
The aim of the diploma thesis is to study and propose improvement of the current convolutional neural network for the classification and detection of fingerprint disease. An improvement of the current convolutional neural network is the change of library for the algorithm of learning, detecting and classifying fingerprint damage. Other improvements are to change the convolutional neural network model and a change in the activation function. At the same time, preprocessing using the Gabor filter will be added. Another change is in the area of thresholding. Next, there will be a change in general-purpose algorithms that will simplify the work for expanding database creation, the learning process itself, the classification and detection process, and the network testing process. At the same time, this network will be expanded with a new prediction and classification. Specifically the damage caused by eczema, psoriasis, pressure and moisture. The improved convolutional neural network is implemented by PyTorch. The network detects which part of the fingerprint is damaged and draws this part into the fingerprint. At the same time, the type of disease or imprint damage is classified during detection. Synthetic fingerprints are used in network training and are supplemented by real fingerprints.
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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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Detection and Classification of Damage in Fingerprint Images Using Neural Nets
Vican, Peter ; Drahanský, Martin (referee) ; Kanich, Ondřej (advisor)
The aim of this diploma thesis is to study and design experimental improvement of the convolutional neural network for disease detection. Another goal is to extend the classifier with a new type of detection. he new type of detection is damage fingerprint by pressure. The experimentally improved convolutional network is implemented by PyTorch. The network detects which part of the fingerprint is damaged and draws this part into the fingerprint. Synthetic fingerprints are used when training the net. Real fingerprints are added to the synthetic fingerprints.
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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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The effect of swimming training on children with skin problems
Kleinerová, Kateřina
The diploma thesis focuses on children of early school age who have undergone mandatory swimming training in pools with various water disinfection methods, specifically chlorine, salt, and UV+. The aim of the thesis is to analyse how this swimming instruction affects children with different skin conditions, such as dry skin, atopic eczema, psoriasis, and plantar warts. The research was conducted at three different primary schools, each of which visited a different swimming pool with a distinct water treatment method during the swimming lessons. A total of 348 students participated in the research, including 172 girls and 176 boys from the 3rd and 4th grades, aged 8 to 11. The research utilized the method of quantitative personal interviewing and the method of qualitative observation. The theoretical part delves into swimming, swimming instruction, water treatment methods in swimming pools, and selected skin diseases. The practical part outlines the research findings. The obtained results demonstrated that skin dryness was observed in 35.4% of students from all three schools participating in swimming lessons. Skin dryness manifested in 26.5% of students in the chlorinated pool, in 46.2% of students in the salt-treated pool, and in 34.6% of students in the UV+ pool. The exacerbation of atopic eczema...
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The effect of swimming training on children with skin problems
Kleinerová, Kateřina ; Svobodová, Irena (advisor) ; Jandová, Soňa (referee)
The diploma thesis focuses on children of early school age who have undergone mandatory swimming training in pools with various water disinfection methods, specifically chlorine, salt, and UV+. The aim of the thesis is to analyse how this swimming instruction affects children with different skin conditions, such as dry skin, atopic eczema, psoriasis, and plantar warts. The research was conducted at three different primary schools, each of which visited a different swimming pool with a distinct water treatment method during the swimming lessons. A total of 348 students participated in the research, including 172 girls and 176 boys from the 3rd and 4th grades, aged 8 to 11. The research utilized the method of quantitative personal interviewing and the method of qualitative observation. The theoretical part delves into swimming, swimming instruction, water treatment methods in swimming pools, and selected skin diseases. The practical part outlines the research findings. The obtained results demonstrated that skin dryness was observed in 35.4% of students from all three schools participating in swimming lessons. Skin dryness manifested in 26.5% of students in the chlorinated pool, in 46.2% of students in the salt-treated pool, and in 34.6% of students in the UV+ pool. The exacerbation of atopic eczema...
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Detection and Classification of Damage in Fingerprint Images Using Neural Nets
Vican, Peter ; Rydlo, Štěpán (referee) ; Kanich, Ondřej (advisor)
The aim of the diploma thesis is to study and propose improvement of the current convolutional neural network for the classification and detection of fingerprint disease. An improvement of the current convolutional neural network is the change of library for the algorithm of learning, detecting and classifying fingerprint damage. Other improvements are to change the convolutional neural network model and a change in the activation function. At the same time, preprocessing using the Gabor filter will be added. Another change is in the area of thresholding. Next, there will be a change in general-purpose algorithms that will simplify the work for expanding database creation, the learning process itself, the classification and detection process, and the network testing process. At the same time, this network will be expanded with a new prediction and classification. Specifically the damage caused by eczema, psoriasis, pressure and moisture. The improved convolutional neural network is implemented by PyTorch. The network detects which part of the fingerprint is damaged and draws this part into the fingerprint. At the same time, the type of disease or imprint damage is classified during detection. Synthetic fingerprints are used in network training and are supplemented by real fingerprints.
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Detection and Classification of Damage in Fingerprint Images Using Neural Nets
Vican, Peter ; Drahanský, Martin (referee) ; Kanich, Ondřej (advisor)
The aim of this diploma thesis is to study and design experimental improvement of the convolutional neural network for disease detection. Another goal is to extend the classifier with a new type of detection. he new type of detection is damage fingerprint by pressure. The experimentally improved convolutional network is implemented by PyTorch. The network detects which part of the fingerprint is damaged and draws this part into the fingerprint. Synthetic fingerprints are used when training the net. Real fingerprints are added to the synthetic fingerprints.
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