• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.10
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• pp.1787-1793
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• 2008

Fingerprint Recognition System is made up of Off-line treatment and On-line treatment; the one is registering all the information of there trieving features which are retrieved in the digitalized fingerprint getting out of the analog fingerprint through the fingerprint acquisition device and the other is the treatment making the decision whether the users are approved to be accessed to the system or not with matching them with the fingerprint features which are retrieved and database from the input fingerprint when the users are approaching the system to use. In this paper, we propose a new data structure, called Union and Division, for processing binarized digital fingerprint image efficiently. We present a minutiae extraction algorithm that is using Union and Division and consists of binarization, noise removal, minutiae extraction stages.

• The KIPS Transactions:PartC
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• v.15C no.6
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• pp.447-454
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• 2008

In this paper, we propose new technique which uses the fingerprint features in order to generate one time passwords(OTPs). Fingerprint is considered to be one of the powerful personal authentication factors and it can be used for generating variable passwords for one time use. Also we performed a simulation of homomorphic graph variable of fingerprint feature point using dendrogram and distribution of fingerprint feature points for proposed password generation method.

• Journal of Advanced Navigation Technology
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• v.13 no.4
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• pp.532-543
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• 2009

As the applications within Internet and Ubiquitous becoming more extensive, the security issues of those applications are appearing to be the most important concern. Therefore, every part of the system should be thoroughly designed and mutually coordinated in order to support overall security of the system. In this paper, we propose new technique which uses the fingerprint features in order to generate Mobile One Time Passwords(OTPs). Fingerprint is considered to be one of the powerful personal authentication factors and it can be used for generating variable passwords for one time use. Also we performed a simulation of homomorphic graph variable of fingerprint feature point using dendrogram and distribution of fingerprint feature points for proposed password generation method.

• Journal of Korea Multimedia Society
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• v.16 no.7
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• pp.870-878
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• 2013

In this paper, we propose a novel fingerprint classification method to enhance the accuracy and efficiency of the fingerprint identification system, one of biometrics systems. According to the previous researches, fingerprints can be categorized into the several patterns based on their pattern of ridges and valleys. After construction of fingerprint database based on their patters, fingerprint classification approach can help to accelerate the fingerprint recognition. The reason is that classification methods reduce the size of the search space to the fingerprints of the same category before matching. First, we suggest a method to extract region of interest (ROI) which have real information about fingerprint from the image. And then we propose a feature extraction method which combines gray level co-occurrence matrix (GLCM) and wavelet features. Finally, we compare the performance of our proposed method with the existing method which use only GLCM as the feature of fingerprint by using the multi-layer perceptron and support vector machine.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.321-324
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• 2002

In this paper, we introduce a new approach to fingerprint classification based on both singular points and gabor features. We find singular points of fingerprint image by using squared direction field and Poincare index. Then, the input fingerprint image can be classified into one of 5 classes using the number of singular points and their location. However, it is often impossible to classify the fingerprint image because the numbers and the position of the singular points are not correct due to noise. In this case Gabor features are extracted from unclassified images using Gator filter and they are classified by using k-NN classifier. This method has been tested on the NIST-4 database. The experimental results show that the proposed method is reliable.

• Journal of the Korean Society of Industry Convergence
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• v.4 no.2
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• pp.177-183
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• 2001

This paper proposes an efficient method for recognizing the fingerprint using the extracted features by adaptive principal component analysis(PCA). The adaptive PCA is implemented by a single-layer neural network for extracting the linear features of fingerprint data. And, the extracted data are transformed into binary data for reducing storage space and transmission time. The proposed method has been applied to recognize the 100 fingerprint data. The simulation results show that the recognitions are all successful and capable of about ${\pm}8^{\circ}$ rotated data.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.295-296
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• 2007

Fake fingersubmission to the sensor is a major problem in fingerprint recognition systems. In this paper, we introduce a novel liveness detection method using multi-static features. For convenience and usefulness of field application, static features are only considered to detect 'live' and 'fake' fingerprint images. Individual pore spacing, noise of image and first order statistics of image are analyzed as our static features to reflect the Physiological and statistical characteristics of live and fake fingerprint.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.5
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• pp.390-395
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• 2000

We present a fingerprint identification algorithm using the wavelet transform and correlation. The wavelet transform is used because of its simple operation to extract fingerprint minutiaes features for fingerprint classification. We perform the rowwise 1-D wavelet transform for a $256\times256$ fingerprint image to get a $1\times256$ column vector using the Haar wavelet and repeat 1-D wavelet transform for a 1$\times$256 column vector to get a $1\times4$ feature vector. Using PNN(Probabilistic Neural Network), we select the possible candidates from the stored feature vectors for fingerprint images. For those candidates, we compute the correlation between the input binary image and the target binary image to find the most similar fingerprint image. The proposed algorithm may be the key to a low cost fingerprint identification system that can be operated on a small computer because it does not need a large memory size and much computation.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.653-662
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• 2022

Recently, various researches have been made on a fingerprint classification method using Convolutional Neural Networks (CNN), which is widely used for multidimensional and complex pattern recognition such as images. The CNN-based fingerprint classification method can be executed by integrating the two-step process, which is generally divided into feature extraction and classification steps. Therefore, since the CNN-based methods can automatically extract features of fingerprint images, they have an advantage of shortening the process. In addition, since they can learn various features of incomplete or low-quality fingerprints, they have flexibility for feature extraction in exceptional situations. In this paper, we intend to identify the research trends of CNN-based fingerprint classification and discuss future direction of research through the analysis of experimental methods and results.