• Analytical Science and Technology
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• v.28 no.1
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• pp.26-32
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• 2015

The consistency and homogeneity of repetitive printing of artificial fingerprint were evaluated using a visual minutiae comparison method and an Automated Fingerprint Identification System (AFIS). The standard latent fingerprint pattern was prepared by the printing of a master digital fingerprint pattern with an inkjet printer cartridge case filled with artificial sweat. The master digital fingerprint pattern was prepared with a scanning of an inked fingerprint pattern of a living subject. The intensities of the master digital fingerprint pattern were adjusted by changing the 'output level' values of the Adobe Photoshop CS 5 software. Number of standard latent fingerprint patterns were printed and then developed with conventional latent fingerprint developing methods; ninhydrin treatment method and 1,2-indandion(1,2-IND)/$ZnCl_2$ treatment method. The ridge details of the latent fingerprint patterns developed with the reagents were visually compared with the inked fingerprint pattern and could confirm that the minutiae of both patterns are visually identical. The ridge detail of the inked fingerprint and reagent developed standard latent fingerprint patterns were compared with an AFIS. The average number of minutiae searched by the AFIS was $52.4{\pm}2.4$ (range = 48~56) for 50 ninhydrin developed latent fingerprint patterns, and $50.2{\pm}1.9$ (range = 47~53) for 50 1,2-IND/$ZnCl_2$ developed latent fingerprint patterns. These low standard deviation values over 50 repetitive printing demonstrated that the 50 standard latent patterns were printed with consistent and homogeneous manner.

• Korean Journal of Clinical Laboratory Science
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• v.56 no.1
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• pp.99-103
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• 2024

Previous artificial latent fingerprint solution has shown unsatisfactory results. Therefore, in this study, we developed an artificial latent fingerprint solution close to the actual fingerprint composition by improving the lipid composition. We mixed lipid solution with amino acid solution at v/v ratios as follows: 2:3, 1:4, 1:5, 1:8, 1:10, 1:20. We then dropped the same amount of each proportion of artificial latent fingerprint solution on porous paper and non-porous slide glass. Subsequently, each sample was treated with Oil red O, Cyanoacrylate fuming and Basic yellow 40 staining. As the concentration of lipids decreased, the output also decreased. Both types of surfaces and all concentrations were visually confirmed very well. In addition, the reactivity to lipids was significantly higher compared to the previous artificial latent fingerprint solution. Furthermore, for the quantitative evaluation, it is necessary to conduct additional research on the printing of the artificial latent fingerprint solution.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.8
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• pp.530-536
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• 2020

The use of minutiae by fingerprint readers is robust against presentation attacks, but one weakness is that the mismatch rate is high. Therefore, minutiae tend to be used with skeleton images. There have been many studies on security vulnerabilities in the characteristics of minutiae, but vulnerability studies on the skeleton are weak, so this study attempts to analyze the vulnerability of presentation attacks against the skeleton. To this end, we propose a method based on the skeleton to recover the original fingerprint using a learning algorithm. The proposed method includes a new learning model, Pix2Pix, which adds a latent vector to the existing Pix2Pix model, thereby generating a natural fingerprint. In the experimental results, the original fingerprint is restored using the proposed machine learning, and then, the restored fingerprint is the input for the fingerprint reader in order to achieve a good recognition rate. Thus, this study verifies that fingerprint readers using the skeleton are vulnerable to presentation attacks. The approach presented in this paper is expected to be useful in a variety of applications concerning fingerprint restoration, video security, and biometrics.