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Basic fingerprint liveness detection
Horák, Tomáš ; Smital, Lukáš (referee) ; Kašpar, Jakub (advisor)
Biometric data is unique, safe and often used to protect information. Even in some cases, detectors can be fooled, and therefore liveness is needed to check. This work focuses on the basic recognition of the liveliness of the fingerprint. Only one finger image is used. For perfect recognition, you need to compare the prints, so have more than one print, and that's why this detection is very demanding. Pre-processing was done with a binary segmentation mask. Methods using gray scale ratios, mean, standard deviations or histogram equalization were also used. All the methods were done on the LivDet 2011 database, in the Matlab environment and the goal was to recognize a live fingerprints from a false ones.
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Basic fingerprint liveness detection
Horák, Tomáš ; Smital, Lukáš (referee) ; Kašpar, Jakub (advisor)
This work focuses on recognizing fingerprints liveness based purely on software-methods evaluating symptoms from just one fingerprint image. At first in this work was described the issue of biometry as such, comparing the advantages and disadvantages of such systems. Next part deal with more detailed process of fingerprint biometry including papillary lines and overall fingerprints as such. In the next phase, the problems and utilization of both software and hardware methods are discussed, including principles of individual approaches. This part is followed by a selection of used fingertip symptoms. This is followed by the practical part and the LivDet 2011 database, which was used for finger recognition. In the practical part is also described the used neural network capturing minor differences in fingerprints according to 13 symptoms.
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Basic fingerprint liveness detection
Horák, Tomáš ; Smital, Lukáš (referee) ; Kašpar, Jakub (advisor)
This work focuses on recognizing fingerprints liveness based purely on software-methods evaluating symptoms from just one fingerprint image. At first in this work was described the issue of biometry as such, comparing the advantages and disadvantages of such systems. Next part deal with more detailed process of fingerprint biometry including papillary lines and overall fingerprints as such. In the next phase, the problems and utilization of both software and hardware methods are discussed, including principles of individual approaches. This part is followed by a selection of used fingertip symptoms. This is followed by the practical part and the LivDet 2011 database, which was used for finger recognition. In the practical part is also described the used neural network capturing minor differences in fingerprints according to 13 symptoms.
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Basic fingerprint liveness detection
Horák, Tomáš ; Smital, Lukáš (referee) ; Kašpar, Jakub (advisor)
Biometric data is unique, safe and often used to protect information. Even in some cases, detectors can be fooled, and therefore liveness is needed to check. This work focuses on the basic recognition of the liveliness of the fingerprint. Only one finger image is used. For perfect recognition, you need to compare the prints, so have more than one print, and that's why this detection is very demanding. Pre-processing was done with a binary segmentation mask. Methods using gray scale ratios, mean, standard deviations or histogram equalization were also used. All the methods were done on the LivDet 2011 database, in the Matlab environment and the goal was to recognize a live fingerprints from a false ones.
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