Relevant bibliographies by topics / Fingerprint Pattern Matching; Minutiae Matching; Fingerprint recognition

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Fingerprint Pattern Matching; Minutiae Matching; Fingerprint recognition'

Author: Grafiati
Published: 3 June 2025
Last updated: 22 June 2025

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Journal articles on the topic "Fingerprint Pattern Matching; Minutiae Matching; Fingerprint recognition"

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CHEN, FANGLIN, MING LI, and YI ZHANG. "A FUSION METHOD FOR PARTIAL FINGERPRINT RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 27, no. 06 (2013): 1356009. http://dx.doi.org/10.1142/s0218001413560090.

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Conventional algorithms for fingerprint recognition are mainly based on minutiae information. However, the small number of minutiae in partial fingerprints is still a challenge in fingerprint matching. In this paper, a novel algorithm is proposed to improve the performance of partial fingerprint matching. A simulation scheme was firstly proposed to construct a serial of partial fingerprints with different area. Then, the influence of the fingerprint area in partial fingerprint recognition is studied. By comparing the performance of partial fingerprint recognition with different fingerprint area, some useful conclusions can be drawn: (1) The decrease of the fingerprint area degrades the performance of partial fingerprint recognition; (2) When the fingerprint area decreases, the genuine matching scores will decrease, whereas the imposter matching scores will increase. Based on these observations, we proposed a fusion scheme based on modified support vector machine (SVM) to combine the area information for fingerprint recognition. Experimental result illustrates the effectiveness of the proposed method.
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G. Nancharaiah, G. Sai Teja Kumari, K. Lakshmi, J. Harika, and B. Srinu. "Fingerprint Recognition and Verification using Fourier Domain Filtering and Histogram Equalization Techniques." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 2 (2024): 698–704. http://dx.doi.org/10.32628/cseit24102100.

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Fingerprint Recognition is a vital method in biometric identification and verification of human beings in various domains like Security, Digital Forensics, Internet of Things (IoT), and many more. Each individual human is having distinct fingerprint pattern than others, hence it is one of the most prominent and widely used method to distinguish individuals. Many research studies and solutions have been developed in biometric domain since a decade, which influences now in making the process of fingerprint recognition more optimized, faster and efficient. However, present fingerprint acquisition/recognition systems have some limitations, mainly longer computation time for fingerprint matching and evaluating the results. This paper presents a procedure for fingerprint matching that takes into account minutiae features in finger print images and the process of creating an OpenCV structure for minutiae extraction and matching of fingerprints.
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Prof., Vikas Goyal* Himanshu Jindal. "IMPROVED FINGER PRINT MATCHING USING MINUTIAE SINGULAR POINTS NETWORK." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 2 (2017): 294–300. https://doi.org/10.5281/zenodo.290194.

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Fingerprints are considered as a unique identification of a person and due to easy access its the best and one of the fastest method used in biometric identification systems. They are unique, so secure and reliable to use and doesnt change for one in a lifetime. And beside these things fingerprint recognition specially using minutiae matching technique is cheap, reliable and accurate up to a satisfactory limits. In this thesis work, we propose a method for fingerprint matching based on minutiae matching. However, unlike conventional minutiae matching algorithms our algorithm also takes into account region and line structures that exist between minutiae pairs. This allows for more structural information of the fingerprint to be accounted for thus resulting in stronger certainty of matching minutiae. Also, since most of the region analysis is preprocessed it does not make the algorithm slower.
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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 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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Ergasheva, Durdona, and Madina Moʻydinova. "ALGORITHMS USED IN FINGERPRINT PATTERN RECOGNITION." MODERN SCIENCE AND RESEARCH 3, no. 1 (2024): 542–45. https://doi.org/10.5281/zenodo.10535702.

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<em>Fingerprint recognition algorithms are used to automatically identify and match the unique characteristics of fingerprints for personal identification purposes. These algorithms may include processing fingerprint images, extracting fingerprint features, generating fingerprint templates, and comparing the templates to a database. There are many fingerprint recognition algorithms that can be used to automatically identify individuals based on fingerprints. This article provides information about the most common algorithms, the minute detail matching algorithm, surface correlation algorithms.</em>
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Prof., Vikas Goyal* Himanshu Jindal. "A REVIEW ON IMPROVED FINGER PRINT MATCHING USING MINUTIAE SINGULAR POINTS NETWORK." INTERNATIONAL JOURNAL OF RESEARCH SCIENCE & MANAGEMENT 4, no. 2 (2017): 19–22. https://doi.org/10.5281/zenodo.272949.

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Fingerprint matching is the process used to determine whether two sets of fingerprint ridge detail come from the same finger. There exist multiple algorithms that do fingerprint matching in many different ways. Some methods involve matching minutiae points between the two images, while others look for similarities in the bigger structure of the fingerprint. In this paper, we provide a study of the existing techniques in the area of finger print matching algorithms.
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Chen, Shu Qian, Yang Lie Fu, and Ming Yang Yin. "Improved Fingerprint Recognition Algorithm Application Study on Smart Home." Advanced Materials Research 734-737 (August 2013): 2970–73. http://dx.doi.org/10.4028/www.scientific.net/amr.734-737.2970.

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Study on a new type of fingerprint identification algorithm and its application in intelligent home electric control lock problem. The traditional fingerprint recognition algorithms on fingerprint minutiae matching accuracy is low, difficult to accurately extract details, leading to lock malfunction or could not be opened. In order to overcome this problem, improved Point pattern fingerprint recognition matching algorithm, joined the matching weight coefficient on the base of pattern matching algorithm, and gives the hardware structure of fingerprint identification system, the improved algorithm is successfully applied in smart home applications, the example shows that, the improved algorithm can effectively improve the recognition rate , reduce false positives, has a certain practical value.
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Esekhaigbe, Emmanuel, and Emmanuel O. Okoduwa. "Design and implementation of a fingerprint-based biometric access control system." Journal of Advances in Science and Engineering 7, no. 1 (2022): 18–23. http://dx.doi.org/10.37121/jase.v7i1.183.

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Security systems are often penetrated by sophisticated criminals, thus there is always a need for new solutions to be devised to give sufficient security to houses and other locations. The goal of this project is to build and deploy a fingerprint-based biometric access control system. The fingerprint is a pattern of ridges and valleys on the surface of a fingertip. Among various biometrics, fingerprint recognition is the most extensively and internationally accepted biometric because of its uniqueness, accuracy, cost-effectiveness, non-transferability, and ease of use. Presented is the system architecture for the system development that demonstrates component augmentation, detail extraction, and matching methodologies. MATLAB and the programming language C were used to develop a software application that was used to build algorithms for improvement, minutiae extraction, and matching processing. The software works by extracting meaningful features known as minutiae points from the person’s fingerprint, then records and stores these minutiae points to verify the person’s identity in the future. The resulting minutiae information is used to find matching fingerprints and to register these fingerprints in the system database. Finally, a verification system and identification system were realized. The proposed automatic door access control system was implemented using the Arduino Atmega 328p microcontroller. The proposed system was tried-out in real-time, and its performance was deemed adequate.
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Alsharman, Nesreen, Adeeb Saaidah, Omar Almomani, Ibrahim Jawarneh, and Laila Al-Qaisi. "Pattern Mathematical Model for Fingerprint Security Using Bifurcation Minutiae Extraction and Neural Network Feature Selection." Security and Communication Networks 2022 (April 16, 2022): 1–16. http://dx.doi.org/10.1155/2022/4375232.

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Biometric based access control is becoming increasingly popular in the current era because of its simplicity and user-friendliness. This eliminates identity recognition manual work and enables automated processing. The fingerprint is one of the most important biometrics that can be easily captured in an uncontrolled environment without human cooperation. It is important to reduce the time consumption during the comparison process in automated fingerprint identification systems when dealing with a large database. Fingerprint classification enables this objective to be accomplished by splitting fingerprints into several categories, but it still poses some difficulties because of the wide intraclass variations and the limited interclass variations since most fingerprint datasets are not categories. In this paper, we propose a classification and matching fingerprint model, and the classification classifies fingerprints into three main categories (arch, loop, and whorl) based on a pattern mathematical model using GoogleNet, AlexNet, and ResNet Convolutional Neural Network (CNN) architecture and matching techniques based on bifurcation minutiae extraction. The proposed model was implemented and tested using MATLAB based on the FVC2004 dataset. The obtained result shows that the accuracy for classification is 100%, 75%, and 43.75% for GoogleNet, ResNet, and AlexNet, respectively. The time required to build a model is 262, 55, and 28 seconds for GoogleNet, ResNet, and AlexNet, respectively.
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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. 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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Dissertations / Theses on the topic "Fingerprint Pattern Matching; Minutiae Matching; Fingerprint recognition"

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Mohamed, Abdul Cader Akmal Jahan. "Finger biometric system using bispectral invariants and information fusion techniques." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/134464/1/Akmal%20Jahan_Mohamed%20Abdul%20Cader_Thesis.pdf.

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Contactless hand biometric systems are better accepted than contact prints as they are hygienic and accelerate data acquisition. This research is one of the few investigating contactless biometrics of the full hand by proposing a novel algorithm based on ridge orientation information along lines connecting key points, higher order spectral features, and fusion. It was investigated with contactless finger images acquired from 81 users, and found to be robust to hand orientation and image size, and provide acceptable performance using two fingers with fusion. The algorithm has potential to use in high throughput applications where contact sensing may be slow.
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Blommé, Johan. "Evaluation of biometric security systems against artificial fingers." Thesis, Linköping University, Department of Electrical Engineering, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1145.

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<p>Verification of users’ identities are normally carried out via PIN-codes or ID- cards. Biometric identification, identification of unique body features, offers an alternative solution to these methods. </p><p>Fingerprint scanning is the most common biometric identification method used today. It uses a simple and quick method of identification and has therefore been favored instead of other biometric identification methods such as retina scan or signature verification. </p><p>In this report biometric security systems have been evaluated based on fingerprint scanners. The evaluation method focuses on copies of real fingers, artificial fingers, as intrusion method but it also mentions currently used algorithms for identification and strengths and weaknesses in hardware solutions used. </p><p>The artificial fingers used in the evaluation were made of gelatin, as it resembles the surface of human skin in ways of moisture, electric resistance and texture. Artificial fingers were based on ten subjects whose real fingers and artificial counterpart were tested on three different fingerprint scanners. All scanners tested accepted artificial fingers as substitutes for real fingers. Results varied between users and scanners but the artificial fingers were accepted between about one forth and half of the times. </p><p>Techniques used in image enhancement, minutiae analysis and pattern matching are analyzed. Normalization, binarization, quality markup and low pass filtering are described within image enhancement. In minutiae analysis connectivity numbers, point identification and skeletonization (thinning algorithms) are analyzed. Within pattern matching, direction field analysis and principal component analysis are described. Finally combinations of both minutiae analysis and pattern matching, hybrid models, are mentioned. </p><p>Based on experiments made and analysis of used techniques a recommendation for future use and development of fingerprint scanners is made.</p>
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Pao-ChengLuo and 羅寶承. "On Adaptive Alignment and Reliable Matching for Minutiae-based Partial Fingerprint Recognition System." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/85382717375648031265.

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Mieloch, Krzysztof. "Hierarchically linked extended features for fingerprint processing." Doctoral thesis, 2008. http://hdl.handle.net/11858/00-1735-0000-0006-B3BC-A.

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Books on the topic "Fingerprint Pattern Matching; Minutiae Matching; Fingerprint recognition"

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J, Grother P., Casasent David Paul, and National Institute of Standards and Technology (U.S.), eds. Distortion-tolerant filter for elastic-distorted fingerprint matching. U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2000.

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J, Grother P., Casasent David Paul, and National Institute of Standards and Technology (U.S.), eds. Distortion-tolerant filter for elastic-distorted fingerprint matching. U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2000.

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J, Grother P., Casasent David Paul, and National Institute of Standards and Technology (U.S.), eds. Distortion-tolerant filter for elastic-distorted fingerprint matching. U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2000.

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J, Grother P., Casasent David Paul, and National Institute of Standards and Technology (U.S.), eds. Distortion-tolerant filter for elastic-distorted fingerprint matching. U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2000.

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J, Grother P., Casasent David Paul, and National Institute of Standards and Technology (U.S.), eds. Distortion-tolerant filter for elastic-distorted fingerprint matching. U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2000.

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1942-, Wilson C. L., and National Institute of Standards and Technology (U.S.), eds. Studies of fingerprint matching using the NIST verification test bed (VTB). U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2003.

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Distortion-tolerant filter for elastic-distorted fingerprint matching. U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2000.

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Distortion-tolerant filter for elastic-distorted fingerprint matching. U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2000.

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Book chapters on the topic "Fingerprint Pattern Matching; Minutiae Matching; Fingerprint recognition"

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Benhammadi, Farid, Hamid Hentous, Kadda Bey-Beghdad, and Mohamed Aissani. "Fingerprint Matching Using Minutiae Coordinate Systems." In Pattern Recognition and Image Analysis. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11492542_65.

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Medina-Pérez, Miguel Angel, Andrés Gutiérrez-Rodríguez, and Milton García-Borroto. "Improving Fingerprint Matching Using an Orientation-Based Minutia Descriptor." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10268-4_14.

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Patel, Ronakkumar B., Dilendra Hiran, and Jayeshbhai Patel. "Biometric Fingerprint Recognition Using Minutiae Score Matching." In Lecture Notes on Data Engineering and Communications Technologies. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4474-3_52.

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Valdes-Ramirez, Danilo, Miguel Angel Medina-Pérez, and Raúl Monroy. "Stacking Fingerprint Matching Algorithms for Latent Fingerprint Identification." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33904-3_21.

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Muñoz-Briseño, Alfredo, Andrés Gago-Alonso, and José Hernández-Palancar. "Fingerprint Matching Using a Geometric Subgraph Mining Approach." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25751-8_20.

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Wanjari, Sunil, S. R. Tandan, and Rohit Miri. "Fingerprint Recognition Technique-Design and Implementation Based on 3D Minutiae Matching Scheme." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-8666-4_25.

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Chau, Alejandro Chau, and Carlos Pon Soto. "Hybrid Algorithm for Fingerprint Matching Using Delaunay Triangulation and Local Binary Patterns." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25085-9_82.

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Nair, Megha M., and S. Likitha. "Pattern Recognition in Fingerprint and DNA Analysis." In Forensic Intelligence and Deep Learning Solutions in Crime Investigation. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-9405-2.ch011.

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In forensic science, DNA and fingerprint analysis are crucial methods that are frequently used to identify people and solve criminal cases. DNA analysis matches biological samples from crime scenes with suspects or identifies victims by using the distinct genetic code present in each person's cells. Forensic scientists can examine even minute amounts of DNA using methods like mitochondrial DNA analysis, short tandem repeat (STR) profiling, and polymerase chain reaction (PCR) amplification, which yields highly accurate human identification. Analyzing fingerprints also depends on the distinct patterns of ridges, whorls, and loops seen on human fingertips. Automated fingerprint identification systems (AFIS) have advanced in recent years and enhanced the speed and accuracy of matching fingerprints across large databases. In forensic investigations, DNA and fingerprint analysis work well together as complementary methods. Fingerprints are direct proof of physical touch, whereas DNA provides a genetic blueprint.
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Thottempudi, Pardhu, and Nagesh Deevi. "Extraction and Matching of Fingerprint Features." In Advances in Civil and Industrial Engineering. IGI Global, 2023. http://dx.doi.org/10.4018/979-8-3693-0044-2.ch005.

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The main objective of the project is to implement fingerprint recognition because it is one of the most popular and reliable methods for human identification and because it makes use of minutiae, which are unique features found in fingerprints. The fingerprint is another type of biometric that is employed to recognize individuals and verify their identities. Extraction of information from fingerprint scans is among the most important steps in fingerprint recognition and classification. The proposed approach for the project relies on utilizing a variety of methods and algorithms to identify fingerprints using the ROI method (i.e., threshold &amp; centroid algorithm). Two human fingerprints can be compared using ROI to determine which has more detail. The main method for highlighting the minute details of the sample fingerprint's fingerprint is FFT extraction. A percentage score is produced as a result of the minute data, and it indicates whether or not two fingerprints match. It was written in MATLAB code.
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Singh, Richa, Mayank Vatsa, and Phalguni Gupta. "Biometrics." In Encyclopedia of Multimedia Technology and Networking, Second Edition. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-014-1.ch017.

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The modern information age gives rise to various challenges, such as organization of society and its security. In the context of organization of society, security has become an important challenge. Because of the increased importance of security and organization, identification and authentication methods have developed into a key technology in various areas, such as entrance control in buildings, access control for automatic teller machines, or in the prominent field of criminal investigation. Identity verification techniques such as keys, cards, passwords, and PIN are widely used security applications. However, passwords or keys may often be forgotten, disclosed, changed, or stolen. Biometrics is an identity verification technique which is being used nowadays and is more reliable, compared to traditional techniques. Biometrics means “life measurement,” but here, the term is associated with the unique characteristics of an individual. Biometrics is thus defined as the “automated methods of identifying or authenticating the identity of a living person, based on physiological or behavioral characteristics.” Physiological characteristics include features such as face, fingerprint, and iris. Behavioral characteristics include signature, gait, and voice. This method of identity verification is preferred over traditional passwords and PIN-based methods for various reasons, such as (Jain, Bolle, &amp; Pankanti, 1999; Jain, Ross, &amp; Prabhakar, 2004): • The person to be identified is required to be physically present for the identity verification. • Identification based on biometric techniques obviates the need to remember a password or carry a token. • It cannot be misplaced or forgotten. Biometrics is essentially a multi-disciplinary area of research, which includes fields like pattern recognition image processing, computer vision, soft computing, and artificial intelligence. For example, face image is captured by a digital camera, which is preprocessed using image enhancement algorithms, and then facial information is extracted and matched. During this process, image processing techniques are used to enhance the face image and pattern recognition, and soft computing techniques are used to extract and match facial features. A biometric system can be either an identification system or a verification (authentication) system, depending on the application. Identification and verification are defined as (Jain et al., 1999, 2004; Ross, Nandakumar, &amp; Jain, 2006): • Identification–One to Many: Identification involves determining a person’s identity by searching through the database for a match. For example, identification is performed in a watch list to find if the query image matches with any of the images in the watch list. • Verification–One to One: Verification involves determining if the identity which the person is claiming is correct or not. Examples of verification include access to an ATM, it can be obtained by matching the features of the individual with the features of the claimed identity in the database. It is not required to perform match with complete database. In this article, we present an overview of the biometric systems and different types of biometric modalities. The next section describes various components of biometric systems, and the third section briefly describes the characteristics of biometric systems. The fourth section provides an overview of different unimodal and multimodal biometric systems. In the fifth section, we have discussed different measures used to evaluate the performance of biometric systems. Finally, we discuss research issues and future directions of biometrics in the last section.
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Conference papers on the topic "Fingerprint Pattern Matching; Minutiae Matching; Fingerprint recognition"

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Lifeng Sha and Xiaoou Tang. "Orientation-improved minutiae for fingerprint matching." In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. IEEE, 2004. http://dx.doi.org/10.1109/icpr.2004.1333795.

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Honglei Wei, Mingen Guo, and Zongying Ou. "Fingerprint Verification Based on Multistage Minutiae Matching." In 18th International Conference on Pattern Recognition (ICPR'06). IEEE, 2006. http://dx.doi.org/10.1109/icpr.2006.578.

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Lifeng Sha, Feng Zhao, and Xiaoou Tang. "Minutiae-based Fingerprint Matching Using Subset Combination." In 18th International Conference on Pattern Recognition (ICPR'06). IEEE, 2006. http://dx.doi.org/10.1109/icpr.2006.802.

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Zhang, Yangyang, Jie Tian, Kai Cao, Peng Li, and Xin Yang. "Improving efficiency of fingerprint matching by minutiae indexing." In 2008 19th International Conference on Pattern Recognition (ICPR). IEEE, 2008. http://dx.doi.org/10.1109/icpr.2008.4761855.

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Dongjin Kwon, Il Dong Yun, Duck Hoon Kim, and Sang Uk Lee. "Fingerprint Matching Method Using Minutiae Clustering and Warping." In 18th International Conference on Pattern Recognition (ICPR'06). IEEE, 2006. http://dx.doi.org/10.1109/icpr.2006.570.

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Cheng, Xi, Sergey Tulyakov, and Venu Govindaraju. "Minutiae-Based Matching State Model for Combinations in Fingerprint Matching System." In 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2013. http://dx.doi.org/10.1109/cvprw.2013.21.

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Jiang Li, Sergey Tulyakov, Faisal Farooq, Jason J. Corso, and Venu Govindaraju. "Integrating minutiae based fingerprint matching with local mutual information." In 2008 19th International Conference on Pattern Recognition (ICPR). IEEE, 2008. http://dx.doi.org/10.1109/icpr.2008.4761888.

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Ng, G. S., X. Tong, X. Tang, and D. Shi. "Adjacent orientation vector based fingerprint minutiae matching system." In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. IEEE, 2004. http://dx.doi.org/10.1109/icpr.2004.1334188.

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Xuefeng Liang and T. Asano. "Fingerprint Matching Using Minutia Polygons." In 18th International Conference on Pattern Recognition (ICPR'06). IEEE, 2006. http://dx.doi.org/10.1109/icpr.2006.571.

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Fan, Dong-Jin, Zi-Rui Deng, and Ju-Fu Feng. "Weight Thin-Plate Spline Fingerprint Matching Using Minutiae Locations and Orientations." In 2008 Chinese Conference on Pattern Recognition. IEEE, 2008. http://dx.doi.org/10.1109/ccpr.2008.66.

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