Pre-registration of translated/distorted fingerprints based on correlation and the orientation field

Lan, Sheng; Guo, Zhenhua; You, Jane

doi:10.1016/j.ins.2020.02.017

Cited by 5 publications

(6 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared to previous studies, the approach offers crucial accomplishments in single fingerprint processing; however, Wan and Zhou [14] reported that the ridge frequency is not constant within the entire fingerprint area. Lan et al [20] proposed the preregistration of distorted fingerprints based on the correlation and orientation fields. The difference map between deformed and reference fingerprints estimates the deformation field based on the correlation and orientation fields.…”

Section: Related Workmentioning

confidence: 99%

“…Similarly, these algorithms should find optimal parameters with maximum ridge matching [15][16][17][18]. Multiple feature-extracting algorithms have been proposed to correct distorted fingerprints [13], [19], [20], [21], [22], [23]. These new algorithms are much more accurate than traditional minutiaebased or ridge-based methods.…”

Section: Introductionmentioning

confidence: 99%

“…Some literature proved validity in the public test databases, including FVC2004 DB1, FVC2006 DB2, Tsinghua DF, NIST SD14, and NIST SD27 [13], [19][20][21][22][23][24][25][26][27], [29]. Application scenarios are limited in practical work due to two primary reasons.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Coarse-to-Fine Approach for Rectifying Distorted Latent Fingerprints From Crime Scenes

Bao,

Wang,

Gao

et al. 2023

IEEE Access

View full text Add to dashboard Cite

With the widely used fingerprints to identify criminals, the influence of fingerprint deformation arouses attention in forensic science. To date, many approaches have been proposed to rectify distorted fingerprints. However, the performance in handling low-quality fingerprints extracted at a crime scene is less high quality than expected. This paper presents a combined method to rectify the latent fingerprints extracted at a crime scene. The method is a coarse-to-fine approach, combining the robustness of traditional pattern recognition and the accuracy of deep learning networks. We conducted several experiments to compare our approach with other approaches, including the nearest-neighbor search and network methods. The results show a remarkable improvement in fingerprint matching, especially in lowquality latent fingerprints. The top 25 cumulative match rate improves from 0.65 (original) to 1 (proposed method), whereas other approaches improve the result to 0.85 at best.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Coarse-to-Fine Approach for Rectifying Distorted Latent Fingerprints From Crime Scenes

Bao,

Wang,

Gao

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…• d [1][2][3][4][5][6]: Each of these features has the distance from the minutia to the closest minutia (d [1]), to the second closest minutia (d [2]), and so on. • r [15,30,45,60,75,90]: Each of these features counts how many minutiae there are in a radius (of 15 pixels, 30 pixels, and so on) around the specified minutia.…”

Section: Predicting the Impact Of Minutiae Errorsmentioning

confidence: 99%

“…Fingerprints are considered genetically unique; up to now, there have been no reports of two individuals having the same fingerprint. Therefore, this biometric feature is widely used in several application fields, such as criminology, banking, and healthcare [2,[12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Fingerprint Matching

Maltoni

Maio

Jain

et al. 2022

Handbook of Fingerprint Recognition

View full text Add to dashboard Cite

Pre-registration of translated/distorted fingerprints based on correlation and the orientation field

Cited by 5 publications

References 12 publications

A Coarse-to-Fine Approach for Rectifying Distorted Latent Fingerprints From Crime Scenes

A Coarse-to-Fine Approach for Rectifying Distorted Latent Fingerprints From Crime Scenes

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

Fingerprint Matching

Contact Info

Product

Resources

About