Impact of Minutiae Errors in Latent Fingerprint Identification: Assessment and Prediction
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-<i>n</i> identification using two d...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-05-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/11/9/4187 |
| _version_ | 1797535223060103168 |
|---|---|
| author | Octavio Loyola-González Emilio Francisco Ferreira Mehnert Aythami Morales Julian Fierrez Miguel Angel Medina-Pérez Raúl Monroy |
| author_facet | Octavio Loyola-González Emilio Francisco Ferreira Mehnert Aythami Morales Julian Fierrez Miguel Angel Medina-Pérez Raúl Monroy |
| author_sort | Octavio Loyola-González |
| collection | DOAJ |
| description | 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-<i>n</i> 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. |
| first_indexed | 2024-03-10T11:42:27Z |
| format | Article |
| id | doaj.art-78f423d44e7042338e01ae53644d319a |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T11:42:27Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-78f423d44e7042338e01ae53644d319a2023-11-21T18:23:33ZengMDPI AGApplied Sciences2076-34172021-05-01119418710.3390/app11094187Impact of Minutiae Errors in Latent Fingerprint Identification: Assessment and PredictionOctavio Loyola-González0Emilio Francisco Ferreira Mehnert1Aythami Morales2Julian Fierrez3Miguel Angel Medina-Pérez4Raúl Monroy5Altair Management Consultants Corp., 303 Wyman St., Suite 300, Waltham, MA 02451, USATecnologico de Monterrey, Carretera al Lago de Guadalupe, Km. 3.5, Atizapán, Estado de Mexico 52926, MexicoBiDA-Lab, Universidad Autonoma de Madrid, Ciudad Universitaria de Cantoblanco, 28049 Madrid, SpainBiDA-Lab, Universidad Autonoma de Madrid, Ciudad Universitaria de Cantoblanco, 28049 Madrid, SpainTecnologico de Monterrey, Carretera al Lago de Guadalupe, Km. 3.5, Atizapán, Estado de Mexico 52926, MexicoTecnologico de Monterrey, Carretera al Lago de Guadalupe, Km. 3.5, Atizapán, Estado de Mexico 52926, MexicoWe 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-<i>n</i> 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.https://www.mdpi.com/2076-3417/11/9/4187latent fingerprintidentificationminutiaebiometric qualityhuman errorperformance evaluation |
| spellingShingle | Octavio Loyola-González Emilio Francisco Ferreira Mehnert Aythami Morales Julian Fierrez Miguel Angel Medina-Pérez Raúl Monroy Impact of Minutiae Errors in Latent Fingerprint Identification: Assessment and Prediction Applied Sciences latent fingerprint identification minutiae biometric quality human error performance evaluation |
| title | Impact of Minutiae Errors in Latent Fingerprint Identification: Assessment and Prediction |
| title_full | Impact of Minutiae Errors in Latent Fingerprint Identification: Assessment and Prediction |
| title_fullStr | Impact of Minutiae Errors in Latent Fingerprint Identification: Assessment and Prediction |
| title_full_unstemmed | Impact of Minutiae Errors in Latent Fingerprint Identification: Assessment and Prediction |
| title_short | Impact of Minutiae Errors in Latent Fingerprint Identification: Assessment and Prediction |
| title_sort | impact of minutiae errors in latent fingerprint identification assessment and prediction |
| topic | latent fingerprint identification minutiae biometric quality human error performance evaluation |
| url | https://www.mdpi.com/2076-3417/11/9/4187 |
| work_keys_str_mv | AT octavioloyolagonzalez impactofminutiaeerrorsinlatentfingerprintidentificationassessmentandprediction AT emiliofranciscoferreiramehnert impactofminutiaeerrorsinlatentfingerprintidentificationassessmentandprediction AT aythamimorales impactofminutiaeerrorsinlatentfingerprintidentificationassessmentandprediction AT julianfierrez impactofminutiaeerrorsinlatentfingerprintidentificationassessmentandprediction AT miguelangelmedinaperez impactofminutiaeerrorsinlatentfingerprintidentificationassessmentandprediction AT raulmonroy impactofminutiaeerrorsinlatentfingerprintidentificationassessmentandprediction |