Next Article in Journal
Aerodynamic Characteristics of Coupled Twin Circular Bridge Hangers with Near Wake Interference
Next Article in Special Issue
Visual Attention Software: A New Tool for Understanding the “Subliminal” Experience of the Built Environment
Previous Article in Journal
Effect of Incense Ash on the Engineering Properties of Cement-Based Composite Material
Previous Article in Special Issue
Biometrics Verification Modality Using Multi-Channel sEMG Wearable Bracelet
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Applied Sciences
Volume 11
Issue 9
10.3390/app11094187
applsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

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

by
Octavio Loyola-González
1,
Emilio Francisco Ferreira Mehnert
2,
Aythami Morales
3,
Julian Fierrez
3,
Miguel Angel Medina-Pérez
2,* and
Raúl Monroy
2
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.
Appl. Sci. 2021, 11(9), 4187; https://doi.org/10.3390/app11094187
Submission received: 30 March 2021 / Revised: 23 April 2021 / Accepted: 24 April 2021 / Published: 4 May 2021
(This article belongs to the Special Issue Advanced Machine Learning Algorithms for Biometrics and Its Applications)
Browse Figures
Versions Notes

Abstract

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.
Keywords: latent fingerprint; identification; minutiae; biometric quality; human error; performance evaluation latent fingerprint; identification; minutiae; biometric quality; human error; performance evaluation

Share and Cite

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, Emilio Francisco Ferreira Mehnert, Aythami Morales, Julian Fierrez, Miguel Angel Medina-Pérez, and Raúl Monroy. 2021. "Impact of Minutiae Errors in Latent Fingerprint Identification: Assessment and Prediction" Applied Sciences 11, no. 9: 4187. https://doi.org/10.3390/app11094187

APA Style

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

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop