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Advanced Generation of Damage Effects into Synthetic Fingerprints
Svoradová, Veronika ; Drahanský, Martin (oponent) ; Kanich, Ondřej (vedoucí práce)
The main goal of this thesis was to design and implement a application that would be able to generate fingerprint damage into a synthetic fingerprint. The application can create fingerprint images damaged by pressure, moisture and skin disease dyshidrosis with different intensity of damage. The application also allows annotation of the generated damage and its export. Selected damages were analyzed before the design was created. A database of fingerprints from five users was created to analyze the damage caused by pressure and moisture. The generated images and the achieved results are tested with VeriFinger and FiQiVi. For testing, 19 sets with fingerprints of different intensity and different type of damage were created. Experiments showed that the quality of the fingerprint decreased the most during the generation of moisture with the highest intensity of damage, where the quality decreased by 61.8 %. This thesis can be used for further research in the field of biometric fingerprint processing.
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Detection and Classification of Damage in Fingerprint Images Using Neural Nets
Vican, Peter ; Rydlo, Štěpán (oponent) ; Kanich, Ondřej (vedoucí práce)
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 (oponent) ; Kanich, Ondřej (vedoucí práce)
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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Detection and Classification of Damage in Fingerprint Images Using Neural Nets
Vican, Peter ; Rydlo, Štěpán (oponent) ; Kanich, Ondřej (vedoucí práce)
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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Advanced Generation of Damage Effects into Synthetic Fingerprints
Svoradová, Veronika ; Drahanský, Martin (oponent) ; Kanich, Ondřej (vedoucí práce)
The main goal of this thesis was to design and implement a application that would be able to generate fingerprint damage into a synthetic fingerprint. The application can create fingerprint images damaged by pressure, moisture and skin disease dyshidrosis with different intensity of damage. The application also allows annotation of the generated damage and its export. Selected damages were analyzed before the design was created. A database of fingerprints from five users was created to analyze the damage caused by pressure and moisture. The generated images and the achieved results are tested with VeriFinger and FiQiVi. For testing, 19 sets with fingerprints of different intensity and different type of damage were created. Experiments showed that the quality of the fingerprint decreased the most during the generation of moisture with the highest intensity of damage, where the quality decreased by 61.8 %. This thesis can be used for further research in the field of biometric fingerprint processing.
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Detection and Classification of Damage in Fingerprint Images Using Neural Nets
Vican, Peter ; Drahanský, Martin (oponent) ; Kanich, Ondřej (vedoucí práce)
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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