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Open AccessArticle

Impact of Minutiae Errors in Latent Fingerprint Identification: Assessment and Prediction

1
Altair Management Consultants Corp., 303 Wyman St., Suite 300, Waltham, MA 02451, USA
2
Tecnologico de Monterrey, Carretera al Lago de Guadalupe, Km. 3.5, Atizapán, Estado de Mexico 52926, Mexico
3
BiDA-Lab, Universidad Autonoma de Madrid, Ciudad Universitaria de Cantoblanco, 28049 Madrid, Spain
*
Author to whom correspondence should be addressed.
Academic Editors: Larbi Boubchir, Elhadj Benkhelifa and Boubaker Daachi
Appl. Sci. 2021, 11(9), 4187; https://doi.org/10.3390/app11094187
Received: 30 March 2021 / Revised: 23 April 2021 / Accepted: 24 April 2021 / Published: 4 May 2021
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. View Full-Text
Keywords: latent fingerprint; identification; minutiae; biometric quality; human error; performance evaluation latent fingerprint; identification; minutiae; biometric quality; human error; performance evaluation
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MDPI and ACS Style

Loyola-González, O.; Ferreira Mehnert, E.F.; Morales, A.; Fierrez, J.; Medina-Pérez, M.A.; Monroy, R. Impact of Minutiae Errors in Latent Fingerprint Identification: Assessment and Prediction. Appl. Sci. 2021, 11, 4187. https://doi.org/10.3390/app11094187

AMA Style

Loyola-González O, Ferreira Mehnert EF, Morales A, Fierrez J, Medina-Pérez MA, Monroy R. Impact of Minutiae Errors in Latent Fingerprint Identification: Assessment and Prediction. Applied Sciences. 2021; 11(9):4187. https://doi.org/10.3390/app11094187

Chicago/Turabian Style

Loyola-González, Octavio; Ferreira Mehnert, Emilio F.; Morales, Aythami; Fierrez, Julian; Medina-Pérez, Miguel A.; Monroy, Raúl. 2021. "Impact of Minutiae Errors in Latent Fingerprint Identification: Assessment and Prediction" Appl. Sci. 11, no. 9: 4187. https://doi.org/10.3390/app11094187

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