• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.73-81
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• 2004

To extract the creditable features in fingerprint image, many people use the thinning algorithm that has a very important position in the preprocessing. In this paper, we propose the robust parallel thinning algorithm that can preserve the connectivity of the binarized fingerprint image, make the thinnest skeleton with 1-pixel width and get near to the medial axis extremely. The proposed thinning method repeats three sub-iterations. The first sub-iteration takes off only the outer boundary pixel by using the interior points. To extract the one side skeletons, the second sub-iteration finds the skeletons with 2-pixel width. The third sub-iteration prunes the needless pixels with 2-pixel width existing in the obtained skeletons and then the proposed thinning algorithm has the robustness against the rotation and noise and can make the balanced medial axis. To evaluate the performance of the proposed thinning algorithm we compare with and analyze the previous algorithms.

• Annual Conference of KIPS
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• 2001.04a
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• pp.139-142
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• 2001

컴퓨터 이미지처리는 여러 분야에서 응용되고 있는데 어떤 특성을 만족하는 객체들의 계수를 자동으로 분류시키는 생물학분야, 편지봉투나 일반양식에 인쇄되어 있는 글자를 자동으로 검출하고 인식하며 초음파검사 혹은 X-Ray 촬영에서 이미지를 획득하여 향상시키는 의료분야, 지문 및 얼굴인식 등에 이용되고 있다. 최근 몇 년 동안 이미지인식, 형태론, 이미지데이터 압축에 관한 연구가 진전되면서 본 연구에서 형태론적인 기법을 사용하여 문자인식을 위한 전처리 혹은 후처리 단계에서 사용되는 이미지향상을 위해서 침식, 골격화의 2단계를 적용하여 기종의 연구 방법과 비교하여 이미지획득 시간을 줄이고 이미지를 향상시켜 문자를 인식하는 알고리즘을 제안한다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.1
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• pp.39-47
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• 2017

Nowadays, there have been an increasing number of illegal use cases where people try to fabricate the working hours by using fake fingerprints. So, the fingerprint liveness detection techniques have been actively studied and widely demanded in various applications. This paper proposes a new method to detect fake fingerprints using CNN (Convolutional Neural Ntworks) based on the patches of fingerprint images. Fingerprint image is divided into small square sized patches and each patch is classified as live, fake, or background by the CNN. Finally, the fingerprint image is classified into either live or fake based on the voting result between the numbers of fake and live patches. The proposed method does not need preprocessing steps such as segmentation because it includes the background class in the patch classification. This method shows promising results of 3.06% average classification errors on LivDet2011, LivDet2013 and LivDet2015 dataset.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.10
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• pp.2317-2328
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• 1997

The objective of this paper is to develop an algorithm for a real-time automatic fingerprint recognition system. The algorithm employs the wavelet transform(WT) and the dominat local orientation that derived from the gradient Gaussian(GoG) and coherence in determining the directions of ridges in fingerprint images. By using the WT, the algorithm does not require conventional preprocessing procedures such as smothing, binarization, thining and restoration. For recognition, two fingerprint images are compared in three different ST domains;one that represents the original image compressed to quarter(LL), another that shows vertical directional characteristic(LH), and third as the block that contains horizontal direction(HL) in WT domain. Each block has dominat local orientation that derived from the GoG and coherence. The proposed algorithm is imprlemented on a SunSparc-2 workstation under X-window environment. Our simulation results, in real-time have shown that while the rate of Type II error-Incorrect recognition of two identical fingerprints as the identical fingerprints-is held at 0%, the rate of Type I error-Incorrect recognitionof two identical fingerprints as the different ones-is 2.5%.

• Journal of the Korea Society of Computer and Information
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• v.10 no.4 s.36
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• pp.203-211
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• 2005

In this paper, we propose a new automatic fingerprint identification system that identifies individuals in large databases. The algorithm consists of three steps; preprocessing, classification, and matching, in the classification. we present a new classification technique based on the statistical approach for directional image distribution. In matching, we also describe improved minutiae candidate pair extraction algorithm that is faster and more accurate than existing algorithm. In matching stage, we extract fingerprint minutiaes from its thinned image for accuracy, and introduce matching process using minutiae linking information. Introduction of linking information into the minutiae matching process is a simple but accurate way, which solves the problem of reference minutiae pair selection in comparison stage of two fingerprints quickly. This algorithm is invariant to translation and rotation of fingerprint. The proposed system was tested on 1000 fingerprint images from the semiconductor chip style scanner. Experimental results reveal false acceptance rate is decreased and genuine acceptance rate is increased than existing method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.4C
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• pp.215-222
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• 2005

In this paper, we propose a fingerprint verification algorithm for the embedded system based on the minutia extracted using the image quality, the minutia structure, and the Sequency and the orientation of ridges. After the pre- and the post-processing, the true minutia are selected, thus it shows high reliability in the fingerprint verification. In matching process, we consider the errors caused by shift, rotation, and pressure when acquiring the fingerprint image and reduce the matching time by applying a local matching instead of a full matching to select the reference pair. The proposed algorithm has been designed and verified in Arm920T environment and various techniques for the realtime process have been applied. Time taken from the fingerprint registration through out the matching is 0.541 second that is relevant for the realtime applications. The FRR (False Reject Rate) and FAR (False Accept Rate) show 0.079 and 0.00005 respectively.

• Journal of Digital Contents Society
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• v.2 no.2
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• pp.179-189
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• 2001

This paper aims at faster processing and retrieval in fingerprint identification systems by reducing the amount of preprocessing and the size of the feature vector. The distribution of fingerprint directions is a set of local directions of ridges and furrows in small overlapped blocks in a fingerprint image. It is extracted initially as a set of 8-direction components through the Gabor filter bank. The discontinuous distribution of directions is smoothed to a continuous one and visualized as a direction image. Then the center of the distribution is selected as a reference point. A feature vector is composed of 192 sine values of the ridge angles at 32-equiangular positions with 6 different distances from the reference point in the direction image. Experiments show that the proposed algorithm performs the same level of correct identification as a conventional algorithm does, while speeding up the overall processing significantly by reducing the length of the feature vector.

• Journal of the Institute of Convergence Signal Processing
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• v.15 no.4
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• pp.135-140
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• 2014

As the digital image devices are widely used, interests in the software- and the hardware-related image processing become higher and the image processing techniques are applied in various fields such as object recognition, object detection, fingerprint recognition, and etc. For the edge detections Sobel, Prewitt, Laplacian, Roberts and Canny detectors are used and these existing methods can excellently detect the edges of the images without noise. However, in the images corrupted by the impulse noise, these methods are insufficent in noise elimination characteristics, showing unsatisfactory edge detection. Therefore in this paper, in order to obtain excellent edge detection characteristics in the corrupted image by the impulse noise, an detection algorithm is porposed, which uses the central pixel of mask divided by four regions along the axis, calculates the estimated mask according to the representing pixel values in each regions, and detects the final edges by applying the estimates mask and the new directional one.

• Journal of KIISE:Software and Applications
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• v.37 no.11
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• pp.859-865
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• 2010

In this paper, a new method for finger vein recognition is proposed. Researchers are recently interested in the finger vein recognition since it is a good way to avoid the forgery in finger prints recognition and the inconveniences in obtaining images of the iris for iris recognition. The vein images are processed to obtain the line shaped vein images through the local histogram equalization and a thinning process. This thinned vein images are processed for matching, using a new matching algorithm, named HS(HeeSung) matching algorithm. This algorithm yields an excellent recognition rate when it is applied to the curve-linear images processed through a thinning or an edge detection. In our experiment with the finger vein images, the recognition rate has reached up to 99.20% using this algorithm applied to 650finger vein images(130person ${\times}$ 5images each). It takes only about 60 milliseconds to match one pair of images.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.5
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• pp.734-739
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• 2018

Fingerprint recognition and iris recognition, which are one of the biometric methods, are easily influenced by external factors such as sunlight. Recently, finger vein recognition is used as a method utilizing internal features. However, for accurate finger vein recognition, it is important to clearly separate vein and background regions. However, it is difficult to separate the vein region and background region due to the abnormalized illumination, and a method of separating the vein region and the background region after normalized the illumination of the input image has been proposed. In this paper, we proposed a method to enhance the quality improvement and improve the processing time compared to the existing finger vein recognition system binarization and labeling method of the image including the image stretching process based on the existing illumination normalization method.