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Journal articles on the topic 'MINUTIA MATCHING'

Author: Grafiati

Published: 11 September 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 (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 fingerpri

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2

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 (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 m

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3

BENHAMMADI, FARID, and KADDA BEGHDAD BEY. "EMBEDDED FINGERPRINT MATCHING ON SMART CARD." International Journal of Pattern Recognition and Artificial Intelligence 27, no. 02 (2013): 1350006. http://dx.doi.org/10.1142/s0218001413500067.

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This paper describes an embedded minutia-based matching algorithm using the reference point neighborhoods minutiae. The proposed matching algorithm is implemented in restricted environments such as smart card devices requiring careful monitoring of both memory and processing time usage. The proposed algorithm uses a circular tessellation to encode fingerprint features in neighborhood minutia localization binary codes. The objective of the present study is the development of a new matching approach which reduces both computing time and required space memory for fingerprint matching on Java Card

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4

Gao, Qinghai. "Toward Constructing Cancellable Templates using K-Nearest Neighbour Method." International Journal of Computer Network and Information Security 9, no. 5 (2017): 1–10. http://dx.doi.org/10.5815/ijcnis.2017.05.01.

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The privacy of biometric data needs to be protected. Cancellable biometrics is proposed as an effective mechanism of protecting biometric data. In this paper a novel scheme of constructing cancellable fingerprint minutiae template is proposed. Specifically, each real minutia point from an original template is mapped to a neighbouring fake minutia in a user-specific randomly generated synthetic template using the k-nearest neighbour method. The recognition template is constructed by collecting the neighbouring fake minutiae of the real minutiae. This scheme has two advantages: (1) An attacker n

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5

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 (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 propos

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6

Soetarmono, Anggya N. D. "IDENTIFIKASI SIDIK JARI DENGAN MENGGUNAKAN STRUKTUR MINUTIA." Teknika 1, no. 1 (2012): 36–46. http://dx.doi.org/10.34148/teknika.v1i1.5.

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Penelitian ini membahas tentang sistem identifikasi personal dengan menggunakan kesesuaian biometrik pada pola sidik jari. Sesuai atau tidak sesuainya sebuah sidik jari dapat diketahui setelah melampaui serangkaian proses. Dimulai dari akuisisi citra sidik jari, memperbaiki kualitas citra, kemudian menggali fitur-fitur minutia yang ada pada citra sidik jari, hingga akhirnya dapat diukur nilai kesesuaiannya. Minutiae based matching adalah metode yang dipilih untuk mengukur tingkat kesesuaian pola sidik jari karena metode ini diyakini akan memberikan tingkat akurasi yang lebih baik jika dibandin

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7

Cao, Kai, Xin Yang, Xinjian Chen, et al. "Minutia handedness: A novel global feature for minutiae-based fingerprint matching." Pattern Recognition Letters 33, no. 10 (2012): 1411–21. http://dx.doi.org/10.1016/j.patrec.2012.03.007.

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8

Soleimani, Hossein, and Mohsen Ahmadi. "Fast and efficient minutia‐based palmprint matching." IET Biometrics 7, no. 6 (2018): 573–80. http://dx.doi.org/10.1049/iet-bmt.2017.0128.

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9

Tico, M., and P. Kuosmanen. "Fingerprint matching using an orientation-based minutia descriptor." IEEE Transactions on Pattern Analysis and Machine Intelligence 25, no. 8 (2003): 1009–14. http://dx.doi.org/10.1109/tpami.2003.1217604.

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10

Selvarani, P., and N. Malarvizhi. "Multibiometric authentication with MATLAB simulation." International Journal of Engineering & Technology 7, no. 1.7 (2018): 47. http://dx.doi.org/10.14419/ijet.v7i1.7.9389.

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Multimodal Biometric Authentication has been used as more security purpose for establishing the user Identification, Authentication and Verification purpose. Multimodal Biometric like Fingerprint and iris are used in this research work for authentication purpose using Matlab simulation. Fingerprint recognition process like Image Enhancement, binarization, Segmentation, thinning, Minutia marking, and Matching are performed with various techniques like Histogram Equalization, Adaptive Binarization, Morphological operations, Minutiae based techniques etc.,Iris recognition process like Segmentatio

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11

Fu, Xiang, and Jufu Feng. "Minutia Tensor Matrix: A New Strategy for Fingerprint Matching." PLOS ONE 10, no. 3 (2015): e0118910. http://dx.doi.org/10.1371/journal.pone.0118910.

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12

Qi, Jin, Suzhen Yang, and Yangsheng Wang. "Fingerprint matching combining the global orientation field with minutia." Pattern Recognition Letters 26, no. 15 (2005): 2424–30. http://dx.doi.org/10.1016/j.patrec.2005.04.016.

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13

Meng, Xianjing, Jinwen Zheng, Xiaoming Xi, Qing Zhang, and Yilong Yin. "Finger vein recognition based on zone-based minutia matching." Neurocomputing 423 (January 2021): 110–23. http://dx.doi.org/10.1016/j.neucom.2020.10.029.

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14

Jiang, Richard M., and Danny Crookes. "FPGA-based minutia matching for biometric fingerprint image database retrieval." Journal of Real-Time Image Processing 3, no. 3 (2008): 177–82. http://dx.doi.org/10.1007/s11554-008-0079-8.

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15

He, Xiaoguang, Jie Tian, Liang Li, Yuliang He, and Xin Yang. "Modeling and Analysis of Local Comprehensive Minutia Relation for Fingerprint Matching." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 37, no. 5 (2007): 1204–11. http://dx.doi.org/10.1109/tsmcb.2006.890285.

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16

Cappelli, Raffaele, Matteo Ferrara, and Davide Maltoni. "Minutia Cylinder-Code: A New Representation and Matching Technique for Fingerprint Recognition." IEEE Transactions on Pattern Analysis and Machine Intelligence 32, no. 12 (2010): 2128–41. http://dx.doi.org/10.1109/tpami.2010.52.

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17

Zhang, Fandong, Shiyuan Xin, and Jufu Feng. "Combining global and minutia deep features for partial high-resolution fingerprint matching." Pattern Recognition Letters 119 (March 2019): 139–47. http://dx.doi.org/10.1016/j.patrec.2017.09.014.

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18

Liu, Eryun, and Qijun Zhao. "Encrypted domain matching of fingerprint minutia cylinder-code (MCC) with l 1 minimization." Neurocomputing 259 (October 2017): 3–13. http://dx.doi.org/10.1016/j.neucom.2016.06.083.

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19

Zhou, Ru, Dexing Zhong, and Jiuqiang Han. "Fingerprint Identification Using SIFT-Based Minutia Descriptors and Improved All Descriptor-Pair Matching." Sensors 13, no. 3 (2013): 3142–56. http://dx.doi.org/10.3390/s130303142.

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20

SURYA RIKIN, A. "A Fingerprint Matching Using Minutia Ridge Shape for Low Cost Match-on-Card Systems." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E88-A, no. 5 (2005): 1305–12. http://dx.doi.org/10.1093/ietfec/e88-a.5.1305.

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21

J.venkatesh, J. venkatesh. "A Study and Analysis of Gabor Filter and K-Nearest Neighbor Approach on Minutia Matching for Fingerprint Recognition." Indian Journal of Applied Research 3, no. 9 (2011): 204–5. http://dx.doi.org/10.15373/2249555x/sept2013/64.

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22

Thangamanimaran, Dhileepan, M. Sharat Chandar, and S. Chandia. "Optimization of biometric recognition using cuckoo search algorithm: a preliminary version for minutia based fingerprint identification." International Journal of Engineering & Technology 7, no. 1.1 (2017): 43. http://dx.doi.org/10.14419/ijet.v7i1.1.8920.

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Currently Behavioural Biometrics is the most widely used means of security. Though Behavioural Biometrics is highly reliable and secure, the data handling process is quite complex. This Problem can be solved by optimizing the process using cuckoo search algorithm.This Paper seeks to optimize the process of fingerprint matching by using an optimal algorithm. The Minutiae in the form of a matrix is extracted from a fingerprint. The Matrix is then split into smaller matrices with increasing dimension and then compared. The matrix with least dimension it is matched. If the Match is true then the v

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23

Bedari, Aseel, Song Wang, and Wencheng Yang. "A Secure Online Fingerprint Authentication System for Industrial IoT Devices over 5G Networks." Sensors 22, no. 19 (2022): 7609. http://dx.doi.org/10.3390/s22197609.

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The development of 5G networks has rapidly increased the use of Industrial Internet of Things (IIoT) devices for control, monitoring, and processing purposes. Biometric-based user authentication can prevent unauthorized access to IIoT devices, thereby safeguarding data security during production. However, most biometric authentication systems in the IIoT have no template protection, thus risking raw biometric data stored as templates in central databases or IIoT devices. Moreover, traditional biometric authentication faces slow, limited database holding capacity and data transmission problems.

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24

Poorna, B., and K. S. Easwarakumar. "Fingerprint Matching Using Recurrent Autoassociative Memory." International Journal of Neural Systems 13, no. 04 (2003): 263–71. http://dx.doi.org/10.1142/s0129065703001583.

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An efficient method for fingerprint searching using recurrent autoassociative memory is proposed. This algorithm uses recurrent autoassociative memory, which uses a connectivity matrix to find if the pattern being searched is already stored in the database. The advantage of this memory is that a big database is to be searched only if there is a matching pattern. Fingerprint comparison is usually based on minutiae matching, and its efficiency depends on the extraction of minutiae. This process may reduce the speed, when large amount of data is involved. So, in the proposed method, a simple appr

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25

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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26

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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27

Kumar, Ravinder. "A Review of Non-Minutiae Based Fingerprint Features." International Journal of Computer Vision and Image Processing 8, no. 1 (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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28

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

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29

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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30

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

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31

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

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32

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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33

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 (2009): 1665–70. http://dx.doi.org/10.1109/tip.2009.2017995.

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34

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 (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 endin

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35

Qiao, Weigao, Jianzhong Zhang, and Fei Yuan. "Fingerprint Matching Method Based on Ant Colony Algorithm." Journal of Physics: Conference Series 2405, no. 1 (2022): 012035. http://dx.doi.org/10.1088/1742-6596/2405/1/012035.

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Abstract Based on previous research, this paper proposes a model and implementation of large-scale fingerprint image retrieval. In the retrieval, the C-means clustering method is used to quickly retrieve fingerprint images, and the shortest path algorithm is used to achieve effective matching fingerprints, and finally, the fingerprints with a higher matching rate are obtained, thereby realizing rapid retrieval and matching of fingerprint IDs. The matching fingerprint minutiae features are analyzed, a retrieval method that can be used for fast fingerprint retrieval is given, and the principle o

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36

Socheat, Sek, and Tianjiang Wang. "Fingerprint Enhancement, Minutiae Extraction and Matching Techniques." Journal of Computer and Communications 08, no. 05 (2020): 55–74. http://dx.doi.org/10.4236/jcc.2020.85003.

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37

Kang, Hyunho, Shoko Yonezawa, Manabu Inuma, Akira Otsuka, and Hideki Imai. "Wolf fingerprints against minutiae count matching systems." IEICE Electronics Express 7, no. 10 (2010): 738–44. http://dx.doi.org/10.1587/elex.7.738.

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38

Choi, Heeseung, Kyoungtaek Choi, and Jaihie Kim. "Fingerprint Matching Incorporating Ridge Features With Minutiae." IEEE Transactions on Information Forensics and Security 6, no. 2 (2011): 338–45. http://dx.doi.org/10.1109/tifs.2010.2103940.

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39

Babatunde, Iwasokun Gabriel. "Fingerprint Matching Using Minutiae-Singular Points Network." International Journal of Signal Processing, Image Processing and Pattern Recognition 8, no. 2 (2015): 375–88. http://dx.doi.org/10.14257/ijsip.2015.8.2.35.

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40

Jeong, Jae-Won, In-Hoon Jang, and Kwee-Bo Sim. "Fingerprint Matching Algorithm Using String-Based MHC Detector Set." Journal of Advanced Computational Intelligence and Intelligent Informatics 9, no. 2 (2005): 175–80. http://dx.doi.org/10.20965/jaciii.2005.p0175.

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Fingerprints have been widely used in the biometric authentication because of its performance, uniqueness and universality. Lately, the speed of identification has become a very important aspect in the fingerprint-based security applications. Also, the reliability still remains the main issue in the fingerprint identification. A fast and reliable fingerprint matching algorithm based on the process of the "self-nonself" discrimination in the biological immune system was proposed. The proposed algorithm is organized by two-matching stages. The 1st matching stage utilized the self-space and MHC d

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41

Singh, Babita, and Waseem Ahmad. "Implementation of Latent Fingerprint Matching System." International Journal of Advance Research and Innovation 2, no. 2 (2014): 43–47. http://dx.doi.org/10.51976/ijari.221406.

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"Biometrics" means "life measurement" .The term is usually associated with the use of unique physiological characteristics to identify an individual. Biometrics is used in computer science as a means of identification and access control. It is also used to identify individuals in groups that are under surveillance. Latent finger prints are inadvertent impressions left by fingers on surfaces of objects. The main difficulties in latent fingerprint matching are unclear ridge structure, small finger area, and large non-linear distortion while rolled fingerprint are of larger size and contain more

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42

Fan, Dong Jin, and Li Dong Wang. "A Novel Fingerprint Matching Algorithm Based on Compatible Multi-Area Alignment." Applied Mechanics and Materials 347-350 (August 2013): 3104–8. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3104.

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Most minutiae-based matching algorithms consist of two phases, local match and global match. In the local phase, some corresponding pairs are obtained by comparing the affine-invariable features of minutiae. And then two images are aligned based on the candidate pairs. However, some spurious candidate pairs and the large nonlinear deformation in images lead to the failure in global match. In this paper, we proposed a novel minutiae-based matching scheme which insert a filtering step after the local match to discard the incompatible pairs and renovate the global match by dividing the whole imag

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43

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 (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 consi

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44

KivutiNjeru, Silas, and Robert Oboko. "Comparative Analysis of Minutiae Based Fingerprint Matching Algorithms." International Journal of Computer Science and Information Technology 8, no. 6 (2016): 59–71. http://dx.doi.org/10.5121/ijcsit.2016.8606.

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45

Chang, Chin-Hsin, Jin-Hong Lin, and Innchyn Her. "New Minutiae-Matching Method Based on Partial Fingerprints." Journal of Imaging Science and Technology 56, no. 1 (2012): 1–10. http://dx.doi.org/10.2352/j.imagingsci.technol.2012.56.1.010503.

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46

V.M., Praseetha, and S. Vadivel. "Enrolment and Matching of Fingerprints using Minutiae Tree." Journal of Computer Science 15, no. 3 (2019): 357–71. http://dx.doi.org/10.3844/jcssp.2019.357.371.

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47

Fanglin Chen, Xiaolin Huang, and Jie Zhou. "Hierarchical Minutiae Matching for Fingerprint and Palmprint Identification." IEEE Transactions on Image Processing 22, no. 12 (2013): 4964–71. http://dx.doi.org/10.1109/tip.2013.2280187.

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48

Tong, Xifeng, Jianhua Huang, Xianglong Tang, and Daming Shi. "Fingerprint minutiae matching using the adjacent feature vector." Pattern Recognition Letters 26, no. 9 (2005): 1337–45. http://dx.doi.org/10.1016/j.patrec.2004.11.012.

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49

He, Yuliang, Jie Tian, Xiping Luo, and Tanghui Zhang. "Image enhancement and minutiae matching in fingerprint verification." Pattern Recognition Letters 24, no. 9-10 (2003): 1349–60. http://dx.doi.org/10.1016/s0167-8655(02)00376-8.

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50

Jie, Ying, Yuan Yi fang, Zhang Renjie, and Song Qifa. "Fingerprint minutiae matching algorithm for real time system." Pattern Recognition 39, no. 1 (2006): 143–46. http://dx.doi.org/10.1016/j.patcog.2005.08.005.

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