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

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

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.65 no.4
• /
• pp.666-672
• /
• 2016

Recently the fingerprint is widely used as one of biometrics to improve the security of financial mobile applications, because of its user convenience and high recognition rate. However, in order to apply fingerprint algorithms to finance and security applications, the recognition rate and processing speed of the fingerprint algorithms have to be improved further. In this paper, we propose the parallel fingerprint enhancement algorithm on general-purpose computing on graphics processing unit (GPGPU) using OpenCL. We discuss the analysis of the parallelism in the fingerprint algorithm as well as the exploration of optimization parameters of the parallel fingerprint algorithm to improve the performance. The experimental results showed that the execution of parallel fingerprint enhancement algorithm on GPGPUs was accelerated from 29.4 upto 69.2 times compared with the execution of the original one on the host CPUs.

• Journal of the Optical Society of Korea
• /
• v.13 no.4
• /
• pp.445-450
• /
• 2009

Fingerprint recognition using a joint transform correlator (JTC) is the most well-known technology among optical fingerprint recognition methods. The JTC method optically compares the reference fingerprint image with the sample fingerprint image then examines match or non-match by acquiring a correlation peak. In contrast to the JTC method, this paper presents a new method to examine fingerprint recognition by producing a computer generated hologram (CGH) of those two fingerprint images and directly comparing them. As a result, we present some parameters to show that fingerprint recognition capability of the CGH direct comparison method is superior to that of the JTC method.

• Bulletin of the Korean Chemical Society
• /
• v.30 no.10
• /
• pp.2287-2293
• /
• 2009

Gas Chromatography-Mass Spectrometry (GC-MS) fingerprint analysis, Principle Components Analysis (PCA), and Hierarchical Cluster Analysis (HCA) were introduced for quality assessment of Curcuma longa L. (C. longa). The GC-MS fingerprint method was developed and validated by analyzing 33 batches of samples of C. longa from different geographic locations. 18 chromatographic peaks were selected as characteristic peaks and their relative peak areas (RPA) were calculated for quantitative expression. Two principal components (PCs) were extracted by PCA. C. longa collected from Guizhou and Fujian were separated from other samples by PC1, capturing 71.83% of variance. While, PC2 contributed for their further separation, capturing 11.13% of variance. HCA confirmed the result of PCA analysis. Therefore, GC-MS fingerprint study with chemometric techniques provides a very flexible and reliable method for quality assessment of C. longa.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.27 no.4A
• /
• pp.345-355
• /
• 2002

This paper describes a new method of directional filter-based analysis for fingerprint enhancement. Fingerprint cages can be represented by direction field of regular structure of ridge patterns. The dominant directional component of ridge plays a very important role in pre-processing steps of fingerprint image analysis such as ridge's linking and noise removal for minutiae extraction. A directional filter bank analyzes input image into directional subband images and synthesizes them to the perfectly reconstructed image. In this paper, a new fingerprint enhancement algorithm based on a directional filter bank is proposed. The algorithm decomposes the fingerprint image into subband images in the analysis stage, accomplishes an enhance procedure by processing subband images in the enhance stage and synthesizes them to the enhanced image in the synthesis stage.

• Phonetics and Speech Sciences
• /
• v.4 no.1
• /
• pp.77-87
• /
• 2012

Interest in music recognition has been growing dramatically after NHN and Daum released their mobile applications for music recognition in 2010. Methods in music recognition based on audio analysis fall into two categories: music recognition using audio fingerprint and Query-by-Singing/Humming (QBSH). While music recognition using audio fingerprint receives music as its input, QBSH involves taking a user-hummed melody. In this paper, research trends are described for music recognition using audio fingerprint, focusing on two methods: one based on fingerprint generation using energy difference between consecutive bands and the other based on hash key generation between peak points. Details presented in the representative papers of each method are introduced.

• Journal of Distribution Science
• /
• v.15 no.10
• /
• pp.15-28
• /
• 2017

Purpose - With the rapid growth of Chinese mobile pay market, it's necessary to run a study of the aims why users prefer to intention of use for mobile fingerprint payment. To reach this goal, UTAUT added Perceived Security and DOI. Research design, data, and methodology - The researchers conducted this study by using collected 3126 responses and the collected data was analyzed by applying statistical techniques factor analysis, AMOS, and Cronbach's Alpha and SPSS 22.0. Results - The result shows that compatibility and relative advantage of mobile fingerprint payment have positive effect on performance expectancy and effort expectancy separately, and the performance expectancy and effort expectancy have positive effect on people's use intention of mobile fingerprint payment. Social influence has a positive effect on the users' use intention of mobile fingerprint payment, Facilitating conditions has a slight effect on the users' use intention of mobile fingerprint payment, Perceived security has the most significant effect on he users' use intention of mobile fingerprint payment. Conclusions - The research showed that compatibility is one of the most important elements that make users continue to use the product. The mobile fingerprint payment must own clearer advantages than other ones that it can reach the biggest market. The Social Influence has a positive influence on the intention of use.

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

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.26 no.5
• /
• pp.1259-1267
• /
• 2016

With the growing use of fingerprint authentication systems in recent years, the fake fingerprint detection is becoming more and more important. This paper mainly proposes a method for fake fingerprint detection based on CNN, it will visualize the distinctive part of detected fingerprint which provides a deeper insight in CNN model. After the preprocessing part using fingerprint segmentation, the pretrained CNN model is used for detecting the liveness detection. Not only a liveness detection but also feature analysis about the live fingerprint and fake fingerprint are provided after classifying which materials are used for making the fake fingerprint. Our system is evaluated on three databases in LivDet2013, which compromise almost 6500 live fingerprint images and 6000 fake fingerprint images in total. The proposed method achieves 3.1% ACE value about the liveness detection and achieves 79.58% accuracy on LiveDet2013.