Impact of minutiae errors in latent fingerprint identification: assessment and prediction
Entity
UAM. Departamento de Tecnología Electrónica y de las ComunicacionesPublisher
MDPIDate
2021-05-04Citation
10.3390/app11094187
Applied Sciences 11.9 (2021): 4187
ISSN
2076-3417DOI
10.3390/app11094187Funded by
This research was partly supported by the National Council of Science and Technology of Mexico under the scholarship grants 717345 and 005438. Authors J.F. and A.M. are funded by project BIBECA (RTI2018-101248-B-I00 MINECO/FEDER) and TRESPASS-ETN (MSCA-ITN-2019-860813)Project
Gobierno de España. RTI2018-101248-B-I00; eu-repo/grantAgrement/EC/H2020/860813/EU//TReSPAsS-ETNEditor's Version
https://doi.org/10.3390/app11094187Subjects
Biometric quality; Human error; Identification; Latent fingerprint; Minutiae; Performance evaluation; TelecomunicacionesRights
© 2021 by the authorsAbstract
We study the impact of minutiae errors in the performance of latent fingerprint identification systems. We perform several experiments in which we remove ground-truth minutiae from latent fingerprints and evaluate the effects on matching score and rank-n identification using two different matchers and the popular NIST SD27 dataset. We observe how missing even one minutia from a fingerprint can have a significant negative impact on the identification performance. Our experimental results show that a fingerprint which has a top rank can be demoted to a bottom rank when two or more minutiae are missed. From our experimental results, we have noticed that some minutiae are more critical than others to correctly identify a latent fingerprint. Based on this finding, we have created a dataset to train several machine learning models trying to predict the impact of each minutia in the matching score of a fingerprint identification system. Finally, our best-trained model can successfully predict if a minutia will increase or decrease the matching score of a latent fingerprint
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Google Scholar:Loyola-González, Octavio
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Mehnert, Emilio Francisco Ferreira
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Morales Moreno, Aythami
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Fiérrez Aguilar, Julián
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Medina-Pérez, Miguel Angel
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Monroy, Raúl
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