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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 (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

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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3

BENHAMMADI, FARID, and KADDA BEGHDAD BEY. "EMBEDDED FINGERPRINT MATCHING ON SMART CARD." International Journal of Pattern Recognition and Artificial Intelligence 27, no. 02 (March 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. The main advantage of our approach is avoiding the implicit alignment of fingerprint images during the matching process while improving the fingerprint verification accuracy. Tests carried out on the public fingerprint databases DB1-a and DB2-a of FVC2002 have shown the effectiveness of our approach.

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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 (May 8, 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 needs to capture both the original template and the synthetic template in order to construct the recognition template; (2) A compromised recognition template can be cancelled easily by replacing the synthetic template. Single-neighboured experiments of self-matching, nonself-matching, and imposter matching are carried out on three databases: DB1B from FVC00, DB1B from FVC02, and DB1 from FVC04. Double-neighboured tests are also conducted for DB1B from FVC02. The results show that the constructed recognition templates can perform more accurately than the original templates and it is feasible to construct cancellable fingerprint templates with the proposed approach.

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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 (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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6

Soetarmono, Anggya N. D. "IDENTIFIKASI SIDIK JARI DENGAN MENGGUNAKAN STRUKTUR MINUTIA." Teknika 1, no. 1 (July 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 dibandingkan dengan metode pattern based matching yang mana pada metode tersebut hanya membandingkan citra dari dua buah sidik jari.

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7

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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8

Soleimani, Hossein, and Mohsen Ahmadi. "Fast and efficient minutia‐based palmprint matching." IET Biometrics 7, no. 6 (April 11, 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 (August 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 (February 5, 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 Segmentation, Normalization, Encoding and Matching are performed with various techniques like Canny edge detection, Daughman’s Rubber sheet model, Hamming Distance etc., can be applied for Fingerprint and iris recognition for authentication purpose. Finally Performance the measure of Precision, Recall, F-Score and Accuracy has evaluated in both fingerprint and iris. It can be concluded Iris Accuracy is higher 0.96% compared with fingerprint accuracy 0.81%.

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11

Fu, Xiang, and Jufu Feng. "Minutia Tensor Matrix: A New Strategy for Fingerprint Matching." PLOS ONE 10, no. 3 (March 30, 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 (November 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 (May 15, 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 (October 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 (December 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 (March 6, 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 (May 1, 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 (October 1, 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 (December 21, 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 verification of next generation bigger matrix is done. If the Match tends to be false then the matrix is skipped and the process is continued for the next matrix in the database. The Process is repeated until accurate match is obtained.Though the time reduced by the optimization of the finger print matching algorithm is insignificant for a smaller data set such as finger print data, it can be a key factor when a larger set of Behavioural biometrics data is considered.

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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 (October 7, 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. To address these issues, in this paper we propose a secure online fingerprint authentication system for IIoT devices over 5G networks. The core of the proposed system is the design of a cancelable fingerprint template, which protects original minutia features and provides privacy and security guarantee for both entity users and the message content transmitted between IIoT devices and the cloud server via 5G networks.Compared with state-of-the-art methods, the proposed authentication system shows competitive performance on six public fingerprint databases, while saving computational costs and achieving fast online matching.

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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 (August 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 approach has been adopted, wherein first determines the closely matched fingerprint images, and then determines the minutiae of only those images for finding the more appropriate one. The gray level value of pixels along with its neighboring ones are considered for the extraction of minutiae, which is more easier than using ridge information. This approach is best suitable when database size is large.

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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 (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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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 (April 18, 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 (January 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 (January 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 (July 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 (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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35

Qiao, Weigao, Jianzhong Zhang, and Fei Yuan. "Fingerprint Matching Method Based on Ant Colony Algorithm." Journal of Physics: Conference Series 2405, no. 1 (December 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 of the method is expounded. The algorithm is used to filter and remove the pseudo-feature points of all fingerprints and is used to calculate the similarity between all minutiae operators. Then the operator with the highest similarity is selected, and the corresponding minutiae pair is used as the registration point pair to complete the two minutiae registration transformation of the point set. And finally, the pairing relationship of minutiae points is established. After that, the time complexity and space complexity of the selected retrieval method is analyzed. All fingerprint pairs with the “identical” relationship provided in the data file are used as query images to verify the retrieval method in the fingerprint dataset. The shortcomings and deficiencies of this method are pointed out, and other ways to solve this problem are introduced.

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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 (June 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 (February 28, 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 (March 20, 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 detector string set that are generated from the information of the minutiae and the values of the directional field. The 2nd matching stage was made based on the local-structure of the minutiae. The proposed matching algorithm reduces matching time while maintaining the reliability of the matching algorithm.

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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 minutiae. Latent fingerprint identification is of critical importance to law enforcement agencies in identifying suspects. While tremendous progress has been made in plain and rolled fingerprint matching, latent fingerprint matching continues to be a difficult problem. The eventual goal of research is to propose a system for matching latent fingerprints to rolled fingerprints that is needed in forensics applications. The system will match latent fingerprints to rolled fingerprints that is needed in forensics applications. In this paper we will apply latent fingerprint algorithm to develop a minutiae-based fingerprint matcher that takes into account the specific characteristics of the latent matching problem.The experimental results indicate that singularity, ridge quality map, and ridge flow map are the most effective features in improving the matching accuracy. The matching module consists of minutiae matching, orientation field matching.

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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 image into small areas according to the location of the candidate pairs. Results on databases of FVC2004 validate our algorithm.

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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 (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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KivutiNjeru, Silas, and Robert Oboko. "Comparative Analysis of Minutiae Based Fingerprint Matching Algorithms." International Journal of Computer Science and Information Technology 8, no. 6 (December 30, 2016): 59–71. http://dx.doi.org/10.5121/ijcsit.2016.8606.

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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 (January 1, 2012): 1–10. http://dx.doi.org/10.2352/j.imagingsci.technol.2012.56.1.010503.

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V.M., Praseetha, and S. Vadivel. "Enrolment and Matching of Fingerprints using Minutiae Tree." Journal of Computer Science 15, no. 3 (March 1, 2019): 357–71. http://dx.doi.org/10.3844/jcssp.2019.357.371.

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

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

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

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

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