A Review of Fingerprint Image Pre-processing

Abbood, Alaa Ahmed; Sulong, Ghazali; Peters, Susan

doi:10.11113/jt.v69.3111

Cited by 6 publications

(6 citation statements)

References 27 publications

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“…Fingerprint pre-processing usually involves enhancing the quality of the fingerprint image, i.e., producing high contrast between ridges and valleys, to facilitate minutiae-based local feature extraction. 15 Fig. 1 Various components involved in minutiae feature-based FBS.…”

Section: Fingerprint Biometric System: An Overviewmentioning

confidence: 99%

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Investigating the impact of thresholding and thinning methods on the performance of partial fingerprint identification systems: a review

2023

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A fingerprint identification system is an application of pattern recognition and image processing. The performance of a fingerprint-based biometric system relies on pre-processing techniques employed on fingerprint images. Especially, thresholding and thinning methods are used to detect minutiae points representing local features and are often utilized to identify a person uniquely. However, studies on partial fingerprints exposed the MasterPrint vulnerability for partial fingerprint identification systems wherein the system performs non-unique user identification. The thresholding and thinning techniques may lead to spurious minutiae generation and stimulate huge MasterPrints. Here, we investigate the impact of thresholding and thinning methods on identification accuracy and the percentage of MasterPrint generated using a partial fingerprint identification method. The experiments comprise four thresholding methods, namely, iterative optimal thresholding, Otsu's global image thresholding, Niblack local thresholding, and Bernsen's local image thresholding. Furthermore, it employs four thinning methods, namely, Khalid, Mariusz, Marek thinning algorithm, Khalid, Marek, Mariusz, Marcin thinning algorithm, Hilditch thinning algorithm, and Stentiford thinning algorithm. The results demonstrate that the identification accuracy and percentage of MasterPrint generated varies significantly by replacing the underlying pre-processing methods. Consequently, each combination of thresholding and thinning methods might not be suitable for user identification in highsecurity applications using a partial fingerprint identification method. The investigation outcomes provide the guidelines to demonstrate the robustness of a partial fingerprint identification method.

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Section: Fingerprint Biometric System: An Overviewmentioning

confidence: 99%

“…In the case of Watchlist ROC plots, ideally, the curves are expected to deviate marginally for DIR and show significant variations on FAR. But the plots from Figs 15. 19 demonstrated that the identification rate reduced considerably compared to FAR.…”

mentioning

confidence: 96%

Investigating the impact of thresholding and thinning methods on the performance of partial fingerprint identification systems: a review

2023

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“…Pre-processing is the stage to normalize the results of the fingerprint image acquisition. Preprocessing consists of sequential processing stages: image cropping, grayscale conversion, image improvement, binary image conversion, and thinning [1].…”

Section: Fingerprint Image Pre-processingmentioning

confidence: 99%

Identification of Biometrics Using Fingerprint Minutiae Extraction Based on Crossing Number Method

Situmorang

2022

JIIKM

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Biometrics based on fingerprint images is a self-recognition technique using fingerprint to represent a person's identity. Fingerprint is characteristic of someone's identity precisely and safely because there are no similarities and cannot be falsified. The purpose of this research is to develop a biometrics identification system based on fingerprint images by utilizing a cell phone camera for the acquisition of fingerprint images. This is based on its simplicity because almost everyone has a cell phone so that a person's identification system based on fingerprint can be used anytime and anywhere. The research was conducted using images generated from cell phone cameras with camera specifications of 2, 5 and 8 mega pixels. The method used in image processing consists of the minutiae crossing number method for the feature extraction process and the minutiae based matching method for the similarity measurement process. The results of the research concluded that cell phone cameras with specifications of 5 and 8 mega pixels can be used for the process of image acquisition in biometrics systems based on fingerprint. The feature extraction process of image results using the minutiae crossing number method and the match measurement process using the minutiae based matching method resulted in an accuracy value of 92.8% on a 5 mega pixel camera and 95.3% on an 8 mega pixel camera. The accuracy value depends on the results of the image acquisition stage, pre-processing, the threshold value in the identification process, and the number of images used in the training data in the database.

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“…One of them focuses on filtering techniques. Kaur and Kaur, Abbod et al, Sawant and Deore, and Rajin and Ajith [5][6][7][8] only listed published work on fingerprint image enhancement.…”

Section: Related Workmentioning

confidence: 99%

“…Biometric sample quality and performance should be the main aspects when assessing a method for fingerprint image enhancement. However, most surveys on fingerprint image enhancement do not perform any assessment at all [2][3][4][5][6][7][8]. Only three publications evaluated biometric performance for a single biometric feature extraction considering at most three datasets [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Survey on the impact of fingerprint image enhancement

Schuch¹,

Schulz²,

Busch³

2017

IET biom.

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The performance of fingerprint comparison algorithms depends on the reliability and accuracy of the features extracted from the fingerprints. The accuracy of the feature extraction algorithms is assumed to depend on the quality of the fingerprint images. Especially, low-quality images can be challenging for feature extraction algorithms. Image enhancement may allow to extract features more accurately. There is a lack of extensive and quantitative evaluation of image enhancement methods. This study investigates the impact of seven typical image enhancement methods on biometric sample quality and on biometric performance. The interrelation of image quality and biometric performance is investigated on 14 datasets. Biometric quality measures are estimated based on image quality metrics NFIQ1 and NFIQ2.0. Biometric performance is tested using MINDTCT and FingerJetFX for feature extraction and BOZORTH3 for biometric comparison. This work shows that the biometric performance can be improved by image enhancement. The significance of improvements depends on both the quality of the datasets and the feature extraction. Thus, there is no single best improvement algorithm. A correlation of changes in scores and image qualities can only be found on the level of entire datasets. No significant correlation can be found for single biometric comparisons.

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A Review of Fingerprint Image Pre-processing

Cited by 6 publications

References 27 publications

Investigating the impact of thresholding and thinning methods on the performance of partial fingerprint identification systems: a review

Investigating the impact of thresholding and thinning methods on the performance of partial fingerprint identification systems: a review

Identification of Biometrics Using Fingerprint Minutiae Extraction Based on Crossing Number Method

Survey on the impact of fingerprint image enhancement

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