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Ocenjevanje kvalitete prstnega odtisa na biometrični napravi
ID KUPINIĆ, HARIS (Author), ID Žabkar, Jure (Mentor) More about this mentor... This link opens in a new window

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Abstract
Identifikacija prstnega odtisa predstavlja eno izmed trenutno najbolj uporabljanih metod verifikacije za pristop do številnih sistemov. Zajem slik je lahko precej kompleksen problem, saj je odvisen od različnih faktorjev, ki močno vplivajo na kvaliteto delovanja senzorja. V diplomski nalogi smo raziskali to področje z vidika umetne inteligence. Predstavljeni pristopi bodo uporabljeni kot varovalka pred uporabo slabih zajemov – izvedli bodo realno analizo slike ter bodo zmožni končnemu uporabniku podati oceno kvalitete njegovega prstnega odtisa. Učna množica, pridobljena s strani ekspertov, predstavlja vhod v model. Izhod je binarna ocena, ki ocenjuje sliko kot “slabo” ali “dobro”.

Language:Slovenian
Keywords:strojno učenje, prstni odtis, biometrija, umetna inteligenca, kvaliteta slik
Work type:Bachelor thesis/paper
Typology:2.11 - Undergraduate Thesis
Organization:FRI - Faculty of Computer and Information Science
Year:2022
PID:20.500.12556/RUL-143509 This link opens in a new window
COBISS.SI-ID:135349507 This link opens in a new window
Publication date in RUL:23.12.2022
Views:483
Downloads:149
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Secondary language

Language:English
Title:Fingerprint image quality assessment on a biometric device
Abstract:
Fingerprint identification represents one of the most popular methods of verification for accessing many systems. Capturing images is pretty complex problem, as it depends on many different factors that highly impact the functionality of the sensor. In this thesis, we have researched this field from the perspective of AI. Used methods will be implemented as safety system as they prevent bad captures – they will analyse the image and give some quality grade of the image to the final user. Used dataset, obtained from the field experts, is used as the input for the model. Output is a binary value, that values an image as a “good” or “bad”.

Keywords:machine learning, fingerprint, biometry, AI, image quality

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