• The Journal of the Korea Contents Association
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• v.17 no.9
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• pp.132-144
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• 2017

The fingerprint classification of categorizing fingerprints by classes should be used in order to improve the processing speed and accuracy in a fingerprint recognition system using a large database. The fingerprint classification methods extract features from the fingerprint ridges of a fingerprint and classify the fingerprint using learning and reasoning techniques based on the classes defined according to the flow and shape of the fingerprint ridges. In earlier days, many researches have been conducted using NIST database acquired by pressing or rolling finger against a paper. However, as automated systems using live-scan scanners for fingerprint recognition have become popular, researches using fingerprint images obtained by live-scan scanners, such as fingerprint data provided by FVC, are increasing. And these days the methods of fingerprint classification using Deep Learning have proposed. In this paper, we investigate the trends of fingerprint classification technology and compare the classification performance of the technology. We desire to assist fingerprint classification research with increasing large fingerprint database in improving the performance by mentioning the necessity of fingerprint classification research with consideration for fingerprint images based on live-scan scanners and analyzing fingerprint classification using deep learning.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.297-300
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• 2003

In this paper, fingerprint minutiae mosaicking algorithm using minutiae of fingerprint is proposed. First, minutiae map is generated from minutiae of fingerprint and minutiae constellation is generated from fingerprint minutiae map. Minutiae constellation is constellation-shaped structure generated from Voronoi Diagram and Delaunay Triangulation using information of minutiae. Secondly, common region is detected by similarity of minutiae constellation of fingerprint minutiae map and minutiae map of individual fingerprint image is composed. Consequently composite minutiae map by mosaicking of fingerprint minutiae improve the performance of the fingerprint matching system.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.2
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• pp.77-85
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• 2010

Fingerprint image quality checking is one of the most important issues in on-line fingerprint recognition because the recognition performance is largely affected by the quality of fingerprint images. In the past, many related fingerprint quality checking methods have typically considered the local quality of fingerprint. However, It is necessary to estimate the global quality of fingerprint to judge whether the fingerprint can be used or not in on-line recognition systems. Therefore, in this paper, we propose both local and global-based methods to calculate the fingerprint quality. Local fingerprint quality checking algorithm considers both the condition of the input fingerprints and orientation estimation errors. The 2D gradients of the fingerprint images were first separated into two sets of 1D gradients. Then，the shapes of the PDFs(Probability Density Functions) of these gradients were measured in order to determine fingerprint quality. And global fingerprint quality checking method uses neural network to estimate the global fingerprint quality based on local quality values. We also analyze the matching performance using FVC2002 database. Experimental results showed that proposed quality check method has better matching performance than NFIQ(NIST Fingerprint Image Quality) method.

• The Journal of the Korea Contents Association
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• v.17 no.12
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• pp.281-288
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• 2017

Financial service commission notified that fingerprint information of their documents should be deleted till 2019 to the financial industry and the public institution. Business solutions for fingerprint detection and masking in document images are introduced. In this paper, a fingerprint information masking algorithm is proposed by using the multiple LBP features to extract fingerprint's intrinsic characteristics for artificial neural network decision whether the candidate is a true fingerprint or not after segmentation of versatile fingerprint candidates from a document image. The experimental results of the proposed fingerprint masking algorithm for 3,497 document images that are saved in a financial industry show that 96.4% of fingerprint information is masked, hence this fingerprint masking algorithm can be used efficiently in real fingerprint masking tasks.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.6
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• pp.10-19
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• 2003

As the technical demand for biometrics is increasing, users expect that fingerprint recognition systems are operable with various fingerprint readers. However, current commercial off-the-shelf fingerprint recognition systems are no interoperable due to the lack of standardization in application program interfaces for fingerprint readers. A cross-matching fingerprint recognition system is a person authentication system based on fingerprints and utilizing different types of fingerprint readers. It should be able to overcome variations in fingerprint images acquired by different readers, such as the size, resolution, contrast of images. The purpose of this research is to develop across-matching fingerprint recognition system for fingerprint research of different sensing mechanism. The fingerprint readers tested in this study are optical, semiconductor and thermal sensor modules, and the prpoposed cross-matching system utilizes both a minutiae-based similarity and a ridge count-based similarity in matching fingerprint images acquired by different sensors.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.163-177
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• 2024

The fingerprint image quality relies on the clearness of separated ridges by valleys and the uniformity of the separation. The condition of skin still dominate the overall quality of the fingerprint. However, the identification performance of such system is very sensitive to the quality of the captured fingerprint image. Fingerprint image quality analysis and enhancement are useful in improving the performance of fingerprint identification systems. A fuzzy technique is introduced in this paper for both fingerprint image quality analysis and enhancement. First, the quality analysis is performed by extracting four features from a fingerprint image which are the local clarity score (LCS), global clarity score (GCS), ridge_valley thickness ratio (RVTR), and the Global Contrast Factor (GCF). A fuzzy logic technique that uses Mamdani fuzzy rule model is designed. The fuzzy inference system is able to analyse and determinate the fingerprint image type (oily, dry or neutral) based on the extracted feature values and the fuzzy inference rules. The percentages of the test fuzzy inference system for each type is as follow: For dry fingerprint the percentage is 81.33, for oily the percentage is 54.75, and for neutral the percentage is 68.48. Secondly, a fuzzy morphology is applied to enhance the dry and oily fingerprint images. The fuzzy morphology method improves the quality of a fingerprint image, thus improving the performance of the fingerprint identification system significantly. All experimental work which was done for both quality analysis and image enhancement was done using the DB_ITS_2009 database which is a private database collected by the department of electrical engineering, institute of technology Sepuluh Nopember Surabaya, Indonesia. The performance evaluation was done using the Feature Similarity index (FSIM). Where the FSIM is an image quality assessment (IQA) metric, which uses computational models to measure the image quality consistently with subjective evaluations. The new proposed system outperformed the classical system by 900% for the dry fingerprint images and 14% for the oily fingerprint images.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.2
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• pp.71-75
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• 2017

In this paper, a smart optical fingerprint sensor technology that is robust against faked fingerprints. A new lens and prism accurately detect fingerprint ridges and valleys that are needed to express a fingerprint's intrinsic characteristics well. The proposed technology includes light path configuration and an optical fingerprint sensor that can effectively identify faked fingerprint features. Results of simulation show the smart optical fingerprint sensor classifies the characteristics of faked fingerprints made from silicone, gelatin, paper, and rubber, and show that the proposed technology has superior detection performance with faked fingerprints, compared to the existing infrared discrimination method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.10
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• pp.1783-1798
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• 2011

Recently, a cryptographic construct, called fuzzy vault, has been proposed for crypto-biometric systems, and some implementations for fingerprint have been reported to protect the stored fingerprint template by hiding the fingerprint features. In this paper, we implement the fuzzy fingerprint vault, combining fingerprint verification and fuzzy vault scheme to protect fingerprint templates. To implement the fuzzy fingerprint vault as a complete system, we have to consider several practical issues such as automatic fingerprint alignment, verification accuracy, execution time, error correcting code, etc. In addition, to protect the fuzzy fingerprint vault from the correlation attack, we propose an approach to insert chaffs in a structured way such that distinguishing the fingerprint minutiae and the chaff points obtained from two applications is computationally hard. Based on the experimental results, we confirm that the proposed approach provides higher security than inserting chaffs randomly without a significant degradation of the verification accuracy, and our implementation can be used for real applications.

• International journal of advanced smart convergence
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• v.6 no.2
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• pp.65-71
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• 2017

Fingerprints are increasingly used for user authentication in small devices such as mobile phones. Therefore, it is important for Fingerprint authentication systems in personal devices to protect the user's fingerprint information while performing efficiently with a lightweight matching algorithm. In this paper, we propose a new method to extract strong minutiae with unique numbers from fingerprint images. Strong minutiae are at all times obtained from fingerprint images, and can be useful for secure and rapid fingerprint authentication. The binary information of strong minutiae of a fingerprint can be transformed securely and can create cancelable fingerprint templates. Also the bit-strings of strong minutiae decrease computing time necessary for the matching procedure between two fingerprints due to the simplicity of bitwise operations. First, we enroll several fingerprints images of a finger. From these images we select a reference fingerprint and put a number on each minutia. Following this procedure, we search for mated-minutiae between the reference fingerprint and other fingerprints one by one. Finally we derive unique numbers of strong minutiae of the finger. In the experiment with the FVC2004 fingerprint database, we show that using the proposed method, strong minutiae can be extracted successfully.

• 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.