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Journal articles on the topic 'Minutiae'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 March 2023

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1

BHOWMICK, PARTHA, ARIJIT BISHNU, BHARGAB BIKRAM BHATTACHARYA, MALAY KUMAR KUNDU, C. A. MURTHY, and TINKU ACHARYA. "DETERMINATION OF MINUTIAE SCORES FOR FINGERPRINT IMAGE APPLICATIONS." International Journal of Image and Graphics 05, no. 03 (July 2005): 537–71. http://dx.doi.org/10.1142/s0219467805001896.

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Many Automatic Fingerprint Identification Systems (AFIS) are based on minutiae matching. Minutiae are the terminations and bifurcations of the ridge lines in a fingerprint image. A gray-scale fingerprint image that has undergone binarization, followed by thinning, in order to extract the minutiae, may contain hundreds of minutiae, all of which are not so vivid and obvious in the original image. Thus, the set of minutiae that are well-defined and more prominent than the rest should be given higher relevance and importance in the process of minutiae matching. In this work, a gray-scale fingerprint image is first preprocessed to produce a thinned binary image. Next, a method to assign a score value to each of the extracted minutiae is proposed, based on certain topographical properties of a minutia. The score associated to a minutia signifies its robustness and prominence. A minutia with a higher score value should be considered with higher priority in the matching scheme to yield better results. Experimental results on several standard databases have been reported.

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2

ZHU, EN, JIAN-PING YIN, GUO-MIN ZHANG, and CHUN-FENG HU. "FINGERPRINT MINUTIAE RELATIONSHIP REPRESENTATION AND MATCHING BASED ON CURVE COORDINATE SYSTEM." International Journal of Image and Graphics 05, no. 04 (October 2005): 729–44. http://dx.doi.org/10.1142/s0219467805001987.

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A minutiae relationship representation and matching method based on curve coordinate system is proposed. For each minutia, a curve coordinate system is established, and the coordinates of other minutiae in this coordinate system is computed. Thus, the coordinate relationship between each pair of minutiae can be evaluated. These relationships are used for pairing minutiae between the template fingerprint and the query fingerprint by means of transferring reference minutiae. The algorithm is tested on FVC2004DBs which include many highly distorted fingerprints. Results have shown that the proposed algorithm achieves improved matching accuracy and is able to cope with highly distorted fingerprints.

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3

Loyola-González, Octavio, Emilio Francisco Ferreira Mehnert, Aythami Morales, Julian Fierrez, Miguel Angel Medina-Pérez, and Raúl Monroy. "Impact of Minutiae Errors in Latent Fingerprint Identification: Assessment and Prediction." Applied Sciences 11, no. 9 (May 4, 2021): 4187. http://dx.doi.org/10.3390/app11094187.

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We study the impact of minutiae errors in the performance of latent fingerprint identification systems. We perform several experiments in which we remove ground-truth minutiae from latent fingerprints and evaluate the effects on matching score and rank-n identification using two different matchers and the popular NIST SD27 dataset. We observe how missing even one minutia from a fingerprint can have a significant negative impact on the identification performance. Our experimental results show that a fingerprint which has a top rank can be demoted to a bottom rank when two or more minutiae are missed. From our experimental results, we have noticed that some minutiae are more critical than others to correctly identify a latent fingerprint. Based on this finding, we have created a dataset to train several machine learning models trying to predict the impact of each minutia in the matching score of a fingerprint identification system. Finally, our best-trained model can successfully predict if a minutia will increase or decrease the matching score of a latent fingerprint.

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4

Jain, Rajneesh, Sheelesh Kr Sharma, and Pankaj Agrawal. "Performance Analysis of Fingerprint Based Image Enhancement and Minutiae Extraction." International Journal of Advanced Research in Computer Science and Software Engineering 8, no. 5 (June 2, 2018): 43. http://dx.doi.org/10.23956/ijarcsse.v8i5.663.

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Extracting minutiae from fingerprint images is one of the most important steps in automatic fingerprint identification and classification. Minutiae are local discontinuities in the fingerprint pattern, mainly terminations and bifurcations. In this work we have propose a method for fingerprint image enhancement. Using histogram equalization over filtering and then minutia are calculated. The results achieved are compared with those obtained through some other methods. The Results show some improvement in the minutiae extraction in terms of quantity.

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5

Cooke, John. "Giant minutiae." New Scientist 203, no. 2720 (August 2009): 25. http://dx.doi.org/10.1016/s0262-4079(09)62087-x.

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6

Coker, David. "Myriad minutiae." New Scientist 203, no. 2724 (September 2009): 27. http://dx.doi.org/10.1016/s0262-4079(09)62341-1.

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7

ZHANG, HONGYUN, DUOQIAN MIAO, and CAIMING ZHONG. "MODIFIED PRINCIPAL CURVES BASED FINGERPRINT MINUTIAE EXTRACTION AND PSEUDO MINUTIAE DETECTION." International Journal of Pattern Recognition and Artificial Intelligence 25, no. 08 (December 2011): 1243–60. http://dx.doi.org/10.1142/s0218001411009135.

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It is difficult but crucial for minutiae extraction and pseudo minutiae deletion of low quality fingerprint images in auto fingerprint identification systems. Traditional methods based on thinning images or gray-level images are, however, susceptible to noise. Reference 14 indicated that principal curves based fingerprint minutiae extraction was feasible to overcome the drawback, but the extended polygonal line (EPL) principal curves algorithm used in the paper extracted the principal curves ineffectively. As the fingerprint data sets are usually large, the original EPL principal curves algorithm is time-consuming. Meanwhile, scattered fingerprint data lead to the deviation of fingerprint skeleton. In this paper, the algorithm is modified, and a fingerprint minutiae extraction and pseudo minutiae detection method based on principal curves is proposed. Experimental results show that the modified EPL principal curves algorithm outperforms the original EPL algorithm both in efficiency and quality, and the proposed minutiae extraction method outperforms the methods proposed by Miao under noise conditions.

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8

Fanglin Chen, Jie Zhou, and Chunyu Yang. "Reconstructing Orientation Field From Fingerprint Minutiae to Improve Minutiae-Matching Accuracy." IEEE Transactions on Image Processing 18, no. 7 (July 2009): 1665–70. http://dx.doi.org/10.1109/tip.2009.2017995.

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9

Cao, Kai, Xin Yang, Xinjian Chen, Xunqiang Tao, Yali Zang, Jimin Liang, and Jie Tian. "Minutia handedness: A novel global feature for minutiae-based fingerprint matching." Pattern Recognition Letters 33, no. 10 (July 2012): 1411–21. http://dx.doi.org/10.1016/j.patrec.2012.03.007.

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10

Popovic, Brankica, and Ljiljana Maskovic. "Fingerprint minutiae filtering based on multiscale directional information." Facta universitatis - series: Electronics and Energetics 20, no. 2 (2007): 233–44. http://dx.doi.org/10.2298/fuee0702233p.

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Automatic identification of humans based on their fingerprints is still one of the most reliable identification methods in criminal and forensic applications, and is widely applied in civil applications as well. Most automatic systems available today use distinctive fingerprint features called minutiae for fingerprint comparison. Conventional feature extraction algorithm can produce a large number of spurious minutiae if fingerprint pattern contains large regions of broken ridges (often called creases). This can drastically reduce the recognition rate in automatic fingerprint identification systems. We can say that for performance of those systems it is more important not to extract spurious (false) minutia even though it means some genuine might be missing as well. In this paper multiscale directional information obtained from orientation field image is used to filter those spurious minutiae, resulting in multiple decrease of their number.

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11

Ginzburg and S. R. Gilbert. "Minutiae, Close-up, Microanalysis." Critical Inquiry 34, no. 1 (2007): 174. http://dx.doi.org/10.2307/4497766.

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12

Ginzburg, Carlo. "Minutiae, Close‐up, Microanalysis." Critical Inquiry 34, no. 1 (September 2007): 174–89. http://dx.doi.org/10.1086/526091.

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13

LOESGH, DANUTA. "MINUTIAE AND CLINICAL GENETICS." Journal of Intellectual Disability Research 17, no. 2 (June 28, 2008): 97–105. http://dx.doi.org/10.1111/j.1365-2788.1973.tb01190.x.

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14

Bishara, Azmi. "The minutiae of racism*." Contemporary Arab Affairs 1, no. 4 (October 1, 2008): 539–50. http://dx.doi.org/10.1080/17550910802391001.

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This article tackles the historical basis and development of the issue of anti-Semitism and examines its perception and impact in the Arab world. The author argues persuasively that anti-Semitism is specific to European racism against Jews. He does not attempt to deflect the term by arguing, as some have done, that Arabs are a Semitic people, but rather unequivocally condemns anti-Semitism and racism of any sort. The author debunks major myths or misconceptions about anti-Semitism and deals frankly with questions of its political utility with regard to Zionism, Israel and Palestine. In the present day, Holocaust denial is unconscionable and, in the end, is not only morally unacceptable, but in the words of the author ‘just plain stupid’. The author castigates Arab and Muslim groups which may take such a stance, arguing that the correct response and Arab reaction to the Holocaust was the simple, straightforward and rational one – a European tragedy, but not one for which the Arabs should assume responsibility.

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15

Santony, Julius. "Minutea Object Extraction in Fingerprint Image Using Morphological Methods and Gabor Filters." KOMTEKINFO 7, no. 1 (January 21, 2020): 32–40. http://dx.doi.org/10.35134/komtekinfo.v7i1.1212.

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Minutiae is part of the fingerprint, which is the point where the fingerprint line stops or branches, which can be observed by scanning at a resolution of 500 pp. a fingerprint has minutiae that range from 50-100 pieces scattered throughout the surface of the fingerprint. To clarify the fingerprint can be done by extracting the minutiae contained in the fingerprint. With this extraction process, fingerprint images can be clarified, so identification of a fingerprint will be easy to do. This research extracts minutiae objects in the fingerprint image, so that the fingerprint line object can be seen clearly. The first stage in this research is object detection and edge detection using morphological methods. The next step is the extraction of minutiae objects with the gabor filter and minutiae extraction . The results obtained can display the fingerprint line of the fingerprint image clearly. From the results of testing 10 fingerprint images proved that the minutiae object in the image can be extracted, so that the fingerprint line of the image is clearer than the original image

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16

Santony, Julius. "Minutea Object Extraction in Fingerprint Image Using Morphological Methods and Gabor Filters (Ekstraksi Objek Minutea Pada Citra Sidik Jari Dengan Metode Morfologi dan Gabor Filter)." Jurnal KomtekInfo 7, no. 1 (January 14, 2020): 32–40. http://dx.doi.org/10.35134/komtekinfo.v7i1.63.

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Minutiae is part of the fingerprint, which is the point where the fingerprint line stops or branches, which can be observed by scanning at a resolution of 500 pp. a fingerprint has minutiae that range from 50-100 pieces scattered throughout the surface of the fingerprint. To clarify the fingerprint can be done by extracting the minutiae contained in the fingerprint. With this extraction process, fingerprint images can be clarified, so identification of a fingerprint will be easy to do. This research extracts minutiae objects in the fingerprint image, so that the fingerprint line object can be seen clearly. The first stage in this research is object detection and edge detection using morphological methods. The next step is the extraction of minutiae objects with the gabor filter and minutiae extraction . The results obtained can display the fingerprint line of the fingerprint image clearly. From the results of testing 10 fingerprint images proved that the minutiae object in the image can be extracted, so that the fingerprint line of the image is clearer than the original image.

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17

Jazi, Ali, and Kadhim Alibraheemi. "Hiding Fingerprint Minutiae In Facefeatures." JOURNAL OF COLLEGE OF EDUCATION FOR PURE SCIENCE 8, no. 3 (September 1, 2018): 89–102. http://dx.doi.org/10.32792/utq.jceps.08.03.09.

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18

Sripavithra, S. "Fingerprint Detection Using Minutiae Extraction." International Journal for Research in Applied Science and Engineering Technology V, no. III (March 30, 2017): 672–75. http://dx.doi.org/10.22214/ijraset.2017.3126.

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19

Roy, Laya K. "Minutiae Based Fingerprint Authentication System." International Journal for Research in Applied Science and Engineering Technology V, no. VIII (August 29, 2017): 578–82. http://dx.doi.org/10.22214/ijraset.2017.8082.

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20

Santhanam, T., C. P. Sumathi, and K. S. Easwarakumar. "Fingerprint minutiae filtering using ARTMAP." Neural Computing and Applications 16, no. 1 (April 28, 2006): 49–55. http://dx.doi.org/10.1007/s00521-006-0054-x.

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21

Chandra, Sarkar N. "Genetics of epidermal ridge minutiae." International Journal of Anthropology 20, no. 1-2 (January 2005): 51–62. http://dx.doi.org/10.1007/bf02445213.

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22

Stoney, David A. "Distribution of epidermal ridge minutiae." American Journal of Physical Anthropology 77, no. 3 (November 1988): 367–76. http://dx.doi.org/10.1002/ajpa.1330770309.

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23

Zhang, Zhen, and Li Liu. "The Research of Algorithms for Fingerprint Characteristic Extraction and Matching." Advanced Materials Research 433-440 (January 2012): 3479–82. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.3479.

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Fingerprint recognition plays an important role in identification of organism characters. Automatic fingerprint identification system(AFIS)is a technology based on computer or microprocessor with advantages of convenience and high efficiency. The extraction and matching of fingerprint minutiae is a necessary step in automatic fingerprint recognition system. A set of algorithms for minutiae extraction and minutiae matching of fingerprint image are proposed in this paper based on the analysis of the inherent minutiae of fingerprint.

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24

Situmorang, Boldson Herdianto. "Identification of Biometrics Using Fingerprint Minutiae Extraction Based on Crossing Number Method." Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika 20, no. 1 (December 2, 2022): 71–80. http://dx.doi.org/10.33751/komputasi.v20i1.6814.

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Biometrics based on fingerprint images is a self-recognition technique using fingerprint to represent a person's identity. Fingerprint is characteristic of someone's identity precisely and safely because there are no similarities and cannot be falsified. The purpose of this research is to develop a biometrics identification system based on fingerprint images by utilizing a cell phone camera for the acquisition of fingerprint images. This is based on its simplicity because almost everyone has a cell phone so that a person's identification system based on fingerprint can be used anytime and anywhere. The research was conducted using images generated from cell phone cameras with camera specifications of 2, 5 and 8 mega pixels. The method used in image processing consists of the minutiae crossing number method for the feature extraction process and the minutiae based matching method for the similarity measurement process. The results of the research concluded that cell phone cameras with specifications of 5 and 8 mega pixels can be used for the process of image acquisition in biometrics systems based on fingerprint. The feature extraction process of image results using the minutiae crossing number method and the match measurement process using the minutiae based matching method resulted in an accuracy value of 92.8% on a 5 mega pixel camera and 95.3% on an 8 mega pixel camera. The accuracy value depends on the results of the image acquisition stage, pre-processing, the threshold value in the identification process, and the number of images used in the training data in the database.

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25

Wang, Bin Bin, Jian De Zheng, and Zhi Qiang Zheng. "Fingerprint Identification Scheme Based on Distribution Density." Applied Mechanics and Materials 539 (July 2014): 117–21. http://dx.doi.org/10.4028/www.scientific.net/amm.539.117.

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Traditional fingerprint identification is adopting minutiae point as a template, but this exist template leaked danger. Based on the distribution density of minutiae point, this paper deeply researches on how to use the distribution density of minutiae point as the template of fingerprints, avoiding directly storing minutiae point data, and ensuring the safety of fingerprint template. At the same time, we proposed a fingerprint matching algorithm based on this template. The experimental results show that the matching algorithm is an effective identification scheme.

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26

Fei, Lunke, Shaohua Teng, Jigang Wu, and Imad Rida. "Enhanced Minutiae Extraction for High-Resolution Palmprint Recognition." International Journal of Image and Graphics 17, no. 04 (October 2017): 1750020. http://dx.doi.org/10.1142/s0219467817500206.

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A palmprint generally possesses about 10 times more minutiae features than a fingerprint, which could provide reliable biometric-based personal authentication. However, wide distribution of various creases in a palmprint creates a number of spurious minutiae. Precisely and efficiently, minutiae extraction is one of the most critical and challenging work for high-resolution palmprint recognition. In this paper, we propose a novel minutiae extraction and matching method for high-resolution palmprint images. The main contributions of this work include the following. First, a circle-boundary consistency is proposed to update the local ridge orientation of some abnormal points. Second, a lengthened Gabor filter is designed to better recover the discontinuous ridges corrupted by wide creases. Third, the principal ridge orientation of palmprint image is calculated to establish an angle alignment system, and coarse-to-fine shifting is performed to obtain the optimal coordinate translation parameters. Following these steps, minutiae matching can be efficiently performed. Experiment results conducted on the public high-resolution palmprint database validate the effectiveness of the proposed method.

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27

Djara, Tahirou, Marc Kokou Assogba, and Antoine Vianou. "A Contactless Fingerprint Verification Method using a Minutiae Matching Technique." International Journal of Computer Vision and Image Processing 6, no. 1 (January 2016): 12–27. http://dx.doi.org/10.4018/ijcvip.2016010102.

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Most of matching or verification phases of fingerprint systems use minutiae types and orientation angle to find matched minutiae pairs from the input and template fingerprints. Unfortunately, due to some non-linear distortions, like excessive pressure and fingers twisting during enrollment, this process can cause the minutiae features to be distorted from the original. The authors are then interested in a fingerprint matching method using contactless images for fingerprint verification. After features extraction, they compute Euclidean distances between template minutiae (bifurcation and ending points) and input image minutiae. They compute then after bifurcation ridges orientation angles and ending point orientations. In the decision stage, they analyze the similarity between templates. The proposed algorithm has been tested on a set of 420 fingerprint images. The verification accuracy is found to be acceptable and the experimental results are promising.

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28

Kumar, Ravinder. "A Review of Non-Minutiae Based Fingerprint Features." International Journal of Computer Vision and Image Processing 8, no. 1 (January 2018): 32–58. http://dx.doi.org/10.4018/ijcvip.2018010103.

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This article presents a critical review of extensive research on automatic fingerprint matching over a decade. In particular, the focus is made on the non-minutiae-based features and machine-learning-based fingerprint matching approaches. This article highlights the problems pertaining to the minutiae-based features and presents a detailed review on the state-of-the-art of non-minutiae-based features. This article also presents an overview of the state-of-the-art fingerprint benchmark databases, along with the open problems and the future directions for the fingerprint matching.

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29

Rashid, Mofeed, and Huda Zaki. "RSA Cryptographic Key Generation Using Fingerprint Minutiae." Iraqi Journal for Computers and Informatics 41, no. 1 (December 31, 2014): 66–69. http://dx.doi.org/10.25195/ijci.v41i1.101.

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Human users find difficult to remember long cryptographic keys. Therefore, researchers, for a long time period, have beeninvestigating ways to use biometric features of the user rather than memorable password or passphrase, in an attempt to produce tough andunrepeatable cryptographic keys and to construct the key unpredictable to a hacker who is deficient of important knowledge about theuser's biometrics. In this paper, generating the strong bio-crypt key based on fingerprint minutiae is presented. At first, the minutiae pointsare extracted from the fingerprint image based on image processing algorithms. Then, the extracted fingerprint minutiae are used forgenerating a 1024 bit prime numbers that used in RSA cypher algorithm to generate 2048 cryptographic key.

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30

Li, Shuiwang, Qijun Zhao, and Xiangdong Fei. "An Improved AM–FM-Based Approach for Reconstructing Fingerprints from Minutiae." International Journal of Image and Graphics 15, no. 01 (January 2015): 1550007. http://dx.doi.org/10.1142/s0219467815500072.

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Reconstructing fingerprint images from a given set of minutiae is an important issue in analyzing the masquerade attack of automated fingerprint recognition systems (AFRSs) and in generating large scale databases of synthetic fingerprint images for the performance evaluation of AFRSs. Existing fingerprint reconstruction methods either cannot generate visually plausible or realistic fingerprint images, or suffer from the occurrence of false minutiae in the reconstructed fingerprint images. In this paper, we analyze the underlying reason of false minutiae generated by state-of-the-art amplitude modulation–frequency modulation (AM–FM)-based methods. Furthermore, we propose an improved approach by devising a better way to cope with the branch cuts (or discontinuities) in the fingerprint ridge orientation fields, and by introducing an effective scheme to remove false minutiae from the reconstructed fingerprint images. Compared with previous AM–FM based methods, the proposed method gets rid of block effects and successfully reduces the number of false minutiae. Theoretic proofs are provided with respect to the effectiveness of the proposed method for fingerprints with multiple singular points. The proposed method has also been evaluated on public fingerprint databases. The results demonstrate that it is superior to the existing methods in reconstructing realistic fingerprint images with fewer false minutiae.

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31

Patel, Ronak B., Dilendra Hiran, and Jayesh M. Patel. "An Algorithm for Fingerprint Minutiae Extraction." International Journal of Computer Sciences and Engineering 7, no. 6 (June 30, 2019): 801–9. http://dx.doi.org/10.26438/ijcse/v7i6.801809.

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32

Chandana, Chandana, Surendra Yadav, and Manish Mathuria. "Fingerprint Recognition based on Minutiae Information." International Journal of Computer Applications 120, no. 10 (June 18, 2015): 39–42. http://dx.doi.org/10.5120/21265-3862.

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33

Mohindru, Pankaj, Govind Sharma, and Pooja Pooja. "Fingerprint Minutiae Extraction using Fuzzy Logic." International Journal of Computer Applications 101, no. 10 (September 18, 2014): 24–26. http://dx.doi.org/10.5120/17724-8086.

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34

Medina-Pérez, Miguel Angel, Milton García-Borroto, Andres Eduardo Gutierrez-Rodríguez, and Leopoldo Altamirano-Robles. "Improving Fingerprint Verification Using Minutiae Triplets." Sensors 12, no. 3 (March 8, 2012): 3418–37. http://dx.doi.org/10.3390/s120303418.

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35

AMELLER, Marco Antonio, and María Angélica GONZÁLEZ. "Minutiae filtering using ridge-valley method." ADCAIJ: ADVANCES IN DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE JOURNAL 5, no. 1 (January 10, 2016): 01. http://dx.doi.org/10.14201/adcaij201651110.

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36

PreetiChaurasia, Om, Saumya RanjanGiri, and Anchal Garg. "A Novel Algorithm for Minutiae Matching." International Journal of Image, Graphics and Signal Processing 4, no. 3 (April 18, 2012): 8–14. http://dx.doi.org/10.5815/ijigsp.2012.03.02.

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37

Haiyun Xu, R. N. J. Veldhuis, A. M. Bazen, T. A. M. Kevenaar, T. A. H. M. Akkermans, and B. Gokberk. "Fingerprint Verification Using Spectral Minutiae Representations." IEEE Transactions on Information Forensics and Security 4, no. 3 (September 2009): 397–409. http://dx.doi.org/10.1109/tifs.2009.2021692.

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38

Creaser, John. "Editing Lycidas: The Authority of Minutiae." Milton Quarterly 44, no. 2 (May 2010): 73–103. http://dx.doi.org/10.1111/j.1094-348x.2010.00245_1.x.

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39

Jianjiang Feng and A. K. Jain. "Fingerprint Reconstruction: From Minutiae to Phase." IEEE Transactions on Pattern Analysis and Machine Intelligence 33, no. 2 (February 2011): 209–23. http://dx.doi.org/10.1109/tpami.2010.77.

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40

Gómez López, Ana María. "Anatomical Minutiae and Cannular Self-experimentation." Performance Research 25, no. 3 (April 2, 2020): 77–82. http://dx.doi.org/10.1080/13528165.2020.1807764.

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41

Zhu, En. "Multiple Reference Minutiae Based Fingerprint Matching." Journal of Computer Research and Development 42, no. 10 (2005): 1733. http://dx.doi.org/10.1360/crad20051014.

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42

Zhou, Baicun, Congying Han, Yonghong Liu, Tiande Guo, and Jin Qin. "Fast minutiae extractor using neural network." Pattern Recognition 103 (July 2020): 107273. http://dx.doi.org/10.1016/j.patcog.2020.107273.

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43

Prabhakar, Salil, Anil K. Jain, and Sharath Pankanti. "Learning fingerprint minutiae location and type." Pattern Recognition 36, no. 8 (August 2003): 1847–57. http://dx.doi.org/10.1016/s0031-3203(02)00322-9.

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44

Espinosa-Duro, V. "Minutiae detection algorithm for fingerprint recognition." IEEE Aerospace and Electronic Systems Magazine 17, no. 3 (March 2002): 7–10. http://dx.doi.org/10.1109/62.990347.

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45

Benhammadi, F., M. N. Amirouche, H. Hentous, K. Bey Beghdad, and M. Aissani. "Fingerprint matching from minutiae texture maps." Pattern Recognition 40, no. 1 (January 2007): 189–97. http://dx.doi.org/10.1016/j.patcog.2006.06.031.

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46

Feng, Jianjiang. "Combining minutiae descriptors for fingerprint matching." Pattern Recognition 41, no. 1 (January 2008): 342–52. http://dx.doi.org/10.1016/j.patcog.2007.04.016.

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47

Sudeepthi, B., Md Imaduddin, and D. Kavitha. "Comparison of Fingerprint Minutiae Matching Technologies." IOSR Journal of Electronics and Communication Engineering 9, no. 6 (2014): 71–76. http://dx.doi.org/10.9790/2834-09617176.

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48

Gao, Qinghai, and Cheng Zhang. "Constructing cancellable template with synthetic minutiae." IET Biometrics 6, no. 6 (June 22, 2017): 448–56. http://dx.doi.org/10.1049/iet-bmt.2016.0192.

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Enesi, Indrit, Miranda Harizaj, and Betim Çiço. "Implementing Fusion Technique Using Biorthogonal Dwt to Increase the Number of Minutiae in Fingerprint Images." Journal of Sensors 2022 (May 24, 2022): 1–13. http://dx.doi.org/10.1155/2022/3502463.

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Abstract:

Biometric devices identify persons based on the minutiae extracted from fingerprint images. Image quality is very important in this process. Usually, fingerprint images have low quality and in many cases they are obtained in various positions. The paper focuses on increasing minutiae detected number by fusing two fingerprint images obtained in various positions. Biorthogonal wavelets have advantages compared to orthogonal wavelets. Fusion is performed in wavelet domain by implementing biorthogonal wavelet. Terminations and bifurcations are extracted from the original and fused images using licensed software Papillon 9.02 and manually extraction by an expert. Biorthogonal Wavelet transform is implemented in the image fusion process, yielding in the increased number of the minutiae compared to the original one. Different biorthogonal wavelets are experimented and various results are obtained. Finding the appropriate wavelet is important in the fusion process since it has a direct impact in the number of minutiae extracted. Based on the number of minutiae and MSE results, the appropriate wavelet to be used in the fusion process is defined.

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BIAN, ZHAOQI, DAVID ZHANG, and WEI SHU. "KNOWLEDGE-BASED FINGERPRINT POST-PROCESSING." International Journal of Pattern Recognition and Artificial Intelligence 16, no. 01 (February 2002): 53–67. http://dx.doi.org/10.1142/s021800140200154x.

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Abstract:

True minutiae extraction in fingerprint image is critical to the performance of an automated identification system. Generally, a set of endings and bifurcations (both called feature points) can be obtained by the thinning image from which the true minutiae of the fingerprint are extracted by using the rules based on the structure of ridges. However, considering some false and true minutiae have similar ridge structures in the thinning image, in a lot of cases, we have to explore their difference in the binary image or the original gray image. In this paper, we first define the different types of feature points and analyze the properties of their ridge structures in both thinning and binary images for the purpose of distinguishing the true and false minutiae. Based on the knowledge of these properties, a fingerprint post-processing approach is developed to eliminate the false minutiae and at the same time improve the thinning image for further application. Many experiments are performed and the results have shown the great effectiveness of the approach.

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