• IEIE Transactions on Smart Processing and Computing
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• v.5 no.1
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• pp.43-48
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• 2016

We propose plain fingerprint classification based on a core stochastic algorithm that effectively uses a core stochastic model, acquiring more fingerprint minutiae and direction, in order to increase matching performance. The proposed core stochastic algorithm uses core presence/absence and contains a ridge direction and distribution map. Simulations show that the fingerprint classification accuracy is improved by more than 14%, on average, compared to other algorithms.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.628-631
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• 1988

This paper describes a fingerprint classification on the basis of feature points(whorl, core) and feature vector and uses a syntactic approach to identify the shape of flow line around the core. Fingerprint image is divided into 8 by 8 subregions and fingerprint region is separated from background. For each subregion of fingerprint region, the dominant ridge direction is obtained to use the slit window quantized in 8 direction and relaxation is performed to correct ridge direction code. Feature points(whorl, core, delta) are found from the ridge direction code. First classification procedure divides the types of fingerprint into 4 class based on whorl and cores. The shape of flow line around the core is obtained by tracing for the fingerprint which has one core or two core and is represented as string. If the string is acceptable by LR(1) parser, feature vector is obtained from feature points(whorl, core, delta) and the shape of flow line around the core. Feature vector is used hierarchically and linearly to classify fingerprint again. The experiment resulted in 97.3 percentages of sucessful classification for 71 fingerprint impressions.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.238-241
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• 2000

A fingerprint core-point detection algorithm is presented in this paper. Core-point is useful for fingerprint classification and also for the fingerprint verification since it giver a reference to a fingerprint. Traditional methods of finding the core-point is introduced. These methods are the method using poincare index and the method using sine component of ridge directions. The proposed method is modified algorithm of the latter using the poincare index. The experimental results show that the proposed algorithm achieves almost the same accuracy with faster speed.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.446-449
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• 2000

A direct RTI(Rotation and translation invariant) fingerprint identification is proposed using the GCMs(generalized complex moments) and Gabor filter-based features from the grey level fingerprint around core point. The core point is located as reference point for the translation invariant matching. And its symmetry axis is detected for the rotation invariant matching from its neighboring region centered at the core point. And then, fingerprint is divided into non-overlapping blocks with respect to the core point and, in contrast to minutiae-based method using various processing steps, features are directly extracted from the blocked grey level fingerprint using Gabor filter, which provides information contained in a particular orientation in the image. The Proposed fingerprint identification is based on the Euclidean distance of the corresponding Gabor features between the input and the template fingerprint. Experiments are conducted on 300 ${\times}$ 300 fingerprints obtained from the CMOS sensor with 500 dpi resolution, and the proposed method could obtain 97% identification rate.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.9C
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• pp.860-867
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• 2003

In this paper, an efficient core point detection method using orientation pattern labeling is proposed in fingerprint image. The core point, which is one of the singular points in fingerprint image, is used as the reference point in the most fingerprint recognizing system. Therefore, the detection of the core point is the most essential step of the fingerprint recognizing system, it can affect in the whole system performance. The proposed method could detect the position of the core point by applying the labeling method for the directional pattern which is come from the distribution of the ridges in fingerprint image and applying detailed algorithms for the decision of the core point's position. The simulation result of proposed method is better than the result of Poincare index method and the sine map method in executing time and detecting rate. Especially, the Poincare index method can't detect the core point in the detection of the arch type and the sine map method takes too much times for executing. But the proposed method can overcome these problems.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.901-904
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• 2000

A direct authentication from gray-scale image, instead of the conventional multi-step preprocessing, is proposed using Gabor filter-based features from the gray-scale fingerprint around core point. The core point is located as a reference point for the translation invariant matching. And its principal symmetry axis is detected for the rotation invariant matching from its neighboring region centered at the core point. And then fingerprint is divided into non-overlapping blocks with respect to the core point and features are directly extracted form the blocked gray level fingerprint using Gabor filter. The proposed fingerprint authentication is based on the Euclidean distance between the corresponding Gabor features of the input and the template fingerprints. Experiments are conducted on 300${\times}$300 fingerprints obtained from a CMOS sensor with 500 dpi resolution, and the proposed method could lower the False Reject Rate(FRR) to 18.2% under False Acceptance Rate(FAR) of 0%.

• The KIPS Transactions:PartB
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• v.10B no.5
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• pp.497-502
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• 2003

Fingerprint classification plays a role of reduction of precise joining time and improvement of the accuracy in a large volume of database. Patterns of fingerprint are classified as 5 patterns : left loop, right loop, arch, whorl, and tented arch by numbers and the location of core point and delta point. The existing fingerprint classification is useful in a captured fingerprint image of core point and delta point using paper and ink. However, this system is unapplicable in modern Automatic Fingerprint Identification System (AFIS) because of problems such as size of input and way of input. To solve the problem, this study is to suggest the way of being able to improve accuracy of fingerprint by fingerprint classification based on the entropy of ridges using fingerprint captured mage of core point and prove this through the experiment.

• Journal of Information Processing Systems
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• v.12 no.1
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• pp.83-99
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• 2016

The latest research on the image-based fingerprint matching approaches indicates that they are less complex than the minutiae-based approaches when it comes to dealing with low quality images. Most of the approaches in the literature are not robust to fingerprint rotation and translation. In this paper, we develop a robust fingerprint matching system by extracting the circular region of interest (ROI) of a radius of 50 pixels centered at the core point. Maximizing their orientation correlation aligns two fingerprints that are to be matched. The modified Euclidean distance computed between the extracted orientation features of the sample and query images is used for matching. Extensive experiments were conducted over four benchmark fingerprint datasets of FVC2002 and two other proprietary databases of RFVC 2002 and the AITDB. The experimental results show the superiority of our proposed method over the well-known image-based approaches in the literature.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.11
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• pp.1005-1010
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• 2012

We propose a novel approach to classify fingerprints using the extracted adaptive core block for improving classification performance of incomplete fingerprints in this paper. We compute representative directions from fingerprint images by the block unit and learn horizontal and vertical Markov models by deciding the center position of a fingerprint image based on the expert knowledge. The center block of a test image is the block has the highest probability after comparing the Markov model with $11{\times}11$ blocks. The proposed approach can effectively classify incomplete fingerprints using the optimal center block.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.1
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• pp.108-113
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• 2007

The classification of fingerprint images is to classify fingerprint images into varies fingerprint types, it is very important in automatic fingerprint recognition. In this paper, a new singular points detection technique was presented. A direction uniform measure is defined to describe the changes of direction fields in a certain neighborhood of fingerprint images. Singular points can be detected by adopting the measure. It should be pointed out that singular points in accurate positions would be obtained in this ways. And an improved Poincare exponential algorithm is presented to identify core points and triangle points. In this paper. making use of 102 experimental fingerprint images datas and attained 7.8% classification errors. This was better than experimental result of abstract [9]. It is possible to use on-line fingerprint images classification.