Journal articles: '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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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.

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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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YOU, XINGE, BIN FANG, YUAN YAN TANG, and ZHENYU HE. "A WAVELET-BASED APPROACH TO RIDGE THINNING IN FINGERPRINT IMAGES." International Journal of Pattern Recognition and Artificial Intelligence 19, no. 05 (2005): 631–45. http://dx.doi.org/10.1142/s0218001405004253.

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As a global feature of fingerprints, the thinning of ridges, extraction of minutiae and computation of orientation field are very important for automatic fingerprint recognition. Many algorithms have been proposed for their computation and estimation, but their results are unsatisfactory, especially for poor quality fingerprint images. In this paper, a robust wavelet-based method to create thinned ridge map of fingerprint for automatic recognition is proposed. Properties of modulus minima based on the spline wavelet function are substantially investigated. Desirable characteristics show that this method is suitable to describe the skeleton of the ridge of the fingerprint image. A multi-scale thinning algorithm based on the modulus minima of wavelet transform is presented. The proposed algorithm is able to improve the skeleton representation of the ridge of the fingerprint without side-effects and limitations of the existing methods. The thinned ridge map can facilitate the extraction of the minutiae for matching in fingerprint recognition. Experiments have been conducted to validate the effectiveness and efficiency of the proposed method.

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Dr., B. Suganthi, Muthulakshmi S., and Nishanthini R. "EFFECTIVE FINGER PRINT RECOGNITION BY USING MULTIMODAL FUSION." International Journal of Engineering Research and Modern Education (IJERME) 5, no. 1 (2020): 1–5. https://doi.org/10.5281/zenodo.3634355.

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Fingerprints techniques are the   most widely used form of biometric identification and security essentials. The fingerprint images may be degraded and corrupted with elements of noise due to many factors including variations in skin and impression conditions, use these fingerprint images for identification and matching purposes it has to be enhanced first. The steps involved in the fingerprint recognition process are Normalization, Orientation Field Estimation, Gabor filtering, Thinning, Binarisation, Minutiae Extraction and False minutiae elimination. In this project we are implementing Normalization and Orientation Field Estimation enhancement techniques using MATLAB. Due to imperfections in the fingerprint image capture process such as non-uniform ink intensity or non-uniform contact with the fingerprint capture device, a fingerprint image may exhibit distorted levels of variation in grey-level values along the ridges and valleys. Thus, normalization is used to reduce the effect of these variations, which facilitates the subsequent image enhancement steps. The orientation field of a fingerprint image defines the local orientation of the ridges contained in the fingerprint. Firstly, the gradient is calculated for every pixel. The orientation vector for each block is derived by performing an averaging operation on all the vectors orthogonal to the gradient pixels in the block. Biometric indenters can be counterfeited, but considered more reliable and secure compared to traditional ID cards or personal passwords methods. Fingerprint pattern fusion improves the performance of a fingerprint recognition system in terms of accuracy and security. Here we are using digital enhancement and fusion approaches that provide highest accuracy of the fingerprint recognition system.

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Adetona, Moses O., and Ayokunle P. Ayeminimowa. "Latent fingerprint development and accuracy using monochrome toner powder in Ibadan, Nigeria." International Journal of Modern Anthropology 3, no. 24 (2025): 262–75. https://doi.org/10.4314/ijma.v3i24.3.

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Dermatoglyphics is an essential component of physical anthropology. It has a wide range of applications such as criminology, population studies, phenotypic genetic studies and plays a crucial role in forensic science. This study aimed to assess the matching accuracy of latent fingerprints on a non-porous surface, with a pre-recorded database of patent fingerprints of a given population. Fingerprints were obtained using Dermalog LF10 fingerprint scanner. One hundred and forty-six (95 patent and 52 latent) medical and dental students of College of Medicine, University of Ibadan were recruited using convenience sampling method. The fingerprints were made up of 650 male and 290 female patent fingerprints and 520 latent prints. Monochrome toner powder was used as a developer of latent fingerprints deposited on glass slides. Four hundred and twenty-two latent prints were well developed and admitted for qualitative and quantitative analysis based on set criteria: pattern recognition and minimum of ten minutiae within pattern area were used as the prerequisites for matching between patent and latent fingerprints. GraphPad Prism 7.0 was used for the test of mean of variables. Ulnar and radial loops pattern were the most common types in both patent and latent prints among males and females. The calculation of matching accuracy, precision, specificity, and sensitivity showed 87.4%, 86.7%, 62.4% and 96.5% respectively. The results of matching demonstrate the reliability and the efficacy of monochrome toner powder as a tool for latent fingerprint development and is thus recommended for latent print development in forensic investigations.

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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 (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 (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 (2006): 143–46. http://dx.doi.org/10.1016/j.patcog.2005.08.005.

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LIANG, XUEFENG, ARIJIT BISHNU, and TETSUO ASANO. "A COMBINATORIAL APPROACH TO FINGERPRINT BINARIZATION AND MINUTIAE EXTRACTION USING EUCLIDEAN DISTANCE TRANSFORM." International Journal of Pattern Recognition and Artificial Intelligence 21, no. 07 (2007): 1141–58. http://dx.doi.org/10.1142/s0218001407005910.

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Most of the fingerprint matching techniques require extraction of minutiae that are ridge endings or bifurcations of ridge lines in a fingerprint image. Crucial to this step is either detecting ridges from the gray-level image or binarizing the image and then extracting the minutiae. In this work, we firstly exploit the property of almost equal width of ridges and valleys for binarization. Computing the width of arbitrary shapes is a nontrivial task. So, we estimate the width using Euclidean distance transform (EDT) and provide a near-linear time algorithm for binarization. Secondly, instead of using thinned binary images for minutiae extraction, we detect minutiae straightaway from the binarized fingerprint images using EDT. We also use EDT values to get rid of spurs and bridges in the fingerprint image. Unlike many other previous methods, our work depends minimally on arbitrary selection of parameters.

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Krishna, Prasad K., and Aithal Sreeramana. "A Conceptual Study on Fingerprint Thinning Process based on Edge Prediction." International Journal of Applied Engineering and Management Letters (IJAEML) 1, no. 2 (2017): 98–111. https://doi.org/10.5281/zenodo.1067110.

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Biometric recognition encompasses numerous modern strategies. Among them, fingerprint reputation is taken into consideration to be the most effective approach for utmost security authentication.  As industrial incentives boom, many new technologies for user identity are being advanced, each with its very own strengths and weaknesses and a potential area of interest marketplace. Fingerprint matching consists of a different process like filtering or preprocessing, binarisation, thinning or skeletonisation, postprocessing, feature extraction, and matching. Out of these fingerprint thinning or skeletonisation is one of the important processes in fingerprint identification or verification systems. Fingerprint thinning or skeletonisation is the manner or technique of lowering the thickness of every line of a fingerprint pattern or ridge pattern to just a single pixel width. After extracting the minutiae from the improved, binarised and thinned image some post-processing is carried out on this final fingerprint image to take away any spurious minutiae. The techniques on this class are of types–crossing number based and morphology-based totally. In this paper even though a new method for thinning is not proposed but a real attempt is made to explain the Edge prediction based thinning process. The Edge Prediction based Skelton formation is totally based on the conditional thinning set of rules, which is used to carry out thinning. The Edge Prediction based thinning process is explained with the help of workflow, algorithm, and flowchart. 

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Tong, Xifeng, Songbo Liu, Jianhua Huang, and Xianglong Tang. "Local relative location error descriptor-based fingerprint minutiae matching." Pattern Recognition Letters 29, no. 3 (2008): 286–94. http://dx.doi.org/10.1016/j.patrec.2007.10.006.

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Perminov, V. N., and A. M. Fartukov. "Method for fingerprint minutiae matching based on their alignment." Pattern Recognition and Image Analysis 17, no. 4 (2007): 631–38. http://dx.doi.org/10.1134/s1054661807040244.

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Zhu, En, Jianping Yin, and Guomin Zhang. "Fingerprint matching based on global alignment of multiple reference minutiae." Pattern Recognition 38, no. 10 (2005): 1685–94. http://dx.doi.org/10.1016/j.patcog.2005.02.016.

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KWAN, PAUL W., JUNBIN GAO, YI GUO, and KEISUKE KAMEYAMA. "A LEARNING FRAMEWORK FOR ADAPTIVE FINGERPRINT IDENTIFICATION USING RELEVANCE FEEDBACK." International Journal of Pattern Recognition and Artificial Intelligence 24, no. 01 (2010): 15–38. http://dx.doi.org/10.1142/s0218001410007841.

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In recent years, law enforcement personnel have greatly been aided by the deployment of Automated Fingerprint Identification Systems (AFIS). These systems largely operate by matching salient features automatically extracted from fingerprint images for their decision. However, there are two major shortcomings in current systems. First, the result of identification depends primarily on the chosen features and the algorithm that matches them. Second, these systems cannot improve their results by benefiting from interactions with seasoned examiners who often can identify minute differences between fingerprints beyond that is capable of by current systems. In this paper, we propose a system for fingerprint identification that incorporates relevance feedback. We show that a persistent semantic space over the database of fingerprints can be incrementally learned. Here, the learning module makes use of a dimensionality reduction process that returns both a low-dimensional semantic space and an out-of-sample mapping function, achieving a two-fold benefits of data compression and the ability to project novel fingerprints directly onto the semantic space for identification. Experimental results demonstrated the potential of this learning framework for adaptive fingerprint identification.

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Maiti, Diptadip, Madhuchhanda Basak, and Debashis Das. "Advancing Fingerprint Template Generation and Matching With Recast Minutiae Clustering and mRBFN." Advances in Artificial Intelligence and Machine Learning 04, no. 01 (2024): 1847–65. http://dx.doi.org/10.54364/aaiml.2024.41107.

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Rapid development of automation in the day-to-day life activity marks up the need of securing bio-metric template and the privacy of rightful owner. Minutiae-based matching is the most popular in the fingerprint recognition system, which greatly suffers from non-linear distortion like translation and rotation. To deal with linear distortion most of the technique proposed in the literature depends upon a reference or singular point. The paper proposes a binary template generation technique which applies an unsupervised clustering technique without fixing the number of clusters. Instead of position and orientation of the minutiae points the cardinality of the clusters are stored and converted into binary template. No spatial pattern information about the fingerprint is stored in the template to protect it from spoofing and information leakage. By the help of modified Radial Basis Function Network (mRBFN) with robust and efficient matching technique the generated templates are matched for authentication. We use MCYT dataset for training the mRBFN. The efficiency of the proposed scheme is evaluated on FVC 2000, FVC 2002 and FVC 2004 dataset.

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MICHAEL, NWOKOLO. "The Value of Fingerprint Science in Crime Detection as Evidence in the Police Criminal Investigation Process in the Society." GVU Journal of Management and Social Sciences 9, no. 1 (2024): 93–105. https://doi.org/10.5281/zenodo.13207951.

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The comparison of Security Systems in advanced countries with that of Nigeria prompted this study, which describes the contribution on how criminals or culprits can easily be detected in the criminal investigation process. This study was conducted in Lagos state. The research assumed a qualitative research method using a survey design, the department of the Central Criminal Registry (CCR) Alagbon Close Force CID Annex Ikoyi, Lagos as the population with the Fingerprint Experts as sample size. The study discovered the uniqueness of Fingerprint Science and adopting it in crime detection during the Police Criminal Investigation Process will bring an awesome change in the entire society. It was discovered that Fingerprint science has a fundamental verification mechanism that identifies individuals on the basis of their physiological features. The effectiveness of Fingerprint Science lies in different recognition processes which include feature-extraction, feature-robustness and feature-matching. The emergence of Fingerprint Science covers a wide range of applications for physical and cybercrime detection. This method also overcomes the loopholes of traditional identification system that were based on probabilities. It is considered as a fundamental shift in the way criminals are detected. Hence, the researcher recommends that this concept should be adopted here and across the entire West African society and government should ensure that the law enforcement agencies understand and apply this concept to justify their criminal investigative   results by ensuring that every member of law enforcement agencies undergo training in Fingerprint Science to put them at an edge to use this concept effectively. Key words: Fingerprint science; Minutiae; Ridge; Pattern Matching; Fingerprint- Recognition Technique

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P., S. Aithal, and Prasad K. Krishna. "Fingerprint Image Segmentation: A Review of State of the Art Techniques." International Journal of Management, Technology, and Social Sciences (IJMTS) 2, no. 2 (2017): 28–39. https://doi.org/10.5281/zenodo.848191.

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In Automatic Fingerprint Identification System (AFIS), pre-processing of the image is a crucial process in deciding the quality and performance of the system. Pre-processing is consists many stages as Segmentation, Enhancement, Binarisation, and Thinning. In this segmentation is one of the steps of pre-processing which differentiate foreground and background region of fingerprint images. Segmentation is the separation of the fingerprint region or extraction of the presence of ridges from the background of the initial image. Segmentation is necessary because it constructs the region of interest from the input image, reduces the processing time, increases the recognition or matching process performance, and reduces the probability of false feature extraction. A 100% accurate segmentation is always very difficult, especially in the very poor quality image or partial image filled with noise such as the presence of latent. Fingerprints are made of Ridge and Valley structure and their features are classified in three levels as Level 1, Level 2, and Level 3. Level 1 Features are singular macro details like ridge pattern and ridge flows. Level 2 is ridge local features like ridge bifurcation and ridge ending or simply minutiae points or ridge orientation. Level 3 is micro details like sweat pores, incipient ridges. This paper provides an overview of the state of the art techniques of fingerprint image segmentation and contribution of other researchers on segmentation. This paper also discusses a different class of segmentation algorithms with its measuring parameters, computational complexity, advantages, limitations, and applications.

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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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Ma, ZhiQiang, XiaoXin Sun, MingJun Cheng, and SuHua Wang. "Research on the application of convolutional-deep neural networks in parallel fingerprint minutiae matching." International Journal of Biometrics 13, no. 1 (2021): 96. http://dx.doi.org/10.1504/ijbm.2021.10034254.

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Wang, SuHua, MingJun Cheng, ZhiQiang Ma, and XiaoXin Sun. "Research on the application of convolutional-deep neural networks in parallel fingerprint minutiae matching." International Journal of Biometrics 13, no. 1 (2021): 96. http://dx.doi.org/10.1504/ijbm.2021.112220.

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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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Bahtiar, Imran, Gunawan Karya, Zohri Muhammad, and Darmawan Bakti Lalu. "Fingerprint Pattern of Matching Family with GLCM Feature." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 4 (2018): 1864–69. https://doi.org/10.12928/TELKOMNIKA.v16i4.8534.

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In this research, fingerprint pattern matching is done to find out whether there is the similarity between parent and child fingerprint pattern. An important step in fingerprint matching is the fingerprint pattern search and matching. Fingerprint data is used by 11 families from various families. The method used in fingerprint feature extraction is GLCM. The GLCM angle used is 0o , and the features used are contrast, homogeneity, correlation, and energy. For fingerprint pattern matching use minutiae score. From the results obtained GLCM has been widely used in fingerprint texture analysis. This study proves that the proposed method for matching fingerprints on parents and children gets the most dominant pattern is the loop pattern.

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Modupe, Agagu. "Development of an Improved Multi-filtering Matching Model for Fingerprint Recognition." Aug-Sept 2023, no. 35 (September 14, 2023): 24–38. http://dx.doi.org/10.55529/jipirs.35.24.38.

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Over the years research done in the area of fingerprint recognition in which the hybrid matching algorithm is one of the most common techniques, though the hybrid algorithm performed well but still faced with the challenge of false minutiae. This study formulated, simulated, and evaluated a multi-filtering fingerprint matching model to develop a multi-filtering matching model for fingerprint recognition. The method employed a multi-filtering model that was formulated using image pre-processing; minutiae feature extraction, post-processing, and cancellation of false minutiae algorithms in the processed images. The model was simulated using Matlab and fingerprint images from the Fingerprint Verification Competition (FVC) 2002 database. The performance of the model was evaluated using the False Acceptance Rate (FAR), False Rejection Rate (FRR), and Error Equal Rate (EER). The results showed that the false minutiae cancellation algorithm considerably reduced the false minutiae points in the thinned images which resulted in the reduction of false acceptance when two different images were tested, and also reduction in false rejection rate when two same images were tested. The match score was below the threshold value of 50 for false acceptance rate and above the threshold value of 50 for the false rejection rate. The error equal rate EER value of 0.076 was recorded. The study concluded that there was a significant reduction in the false minutiae points present in the thinned images and that a high accuracy of fingerprint matching was achieved when the datasets include poor quality fingerprint images.

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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 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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Krishna, Mr Hari. "Blood Group Detection using Finger Print." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 2209–13. https://doi.org/10.22214/ijraset.2025.67757.

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The most trustworthy and distinctive aspect of human identity is the fingerprint pattern. The fingerprint pattern cannot be altered and stays as it is till death of a person. Up to this date in the cases of events consideration fingerprint evidence is regarded as most crucial evidence even in court of law. The minutiae pattern of every human is distinct and the possibility of having similarity is very rare almost one in sixty-four thousand million. The minutiae pattern varies even in twins. The ridge pattern is also distinct and does not change from birth of individual. The approach provided in this paper include matching of minutiae feature pattern obtained from fingerprint for person identification system. The issue of blood group is also researched with the assistance of fingerprint. The fingerprint matching is processed with the estimation of ridge frequency. The spatial features for this purpose are extracted via Gabor filter. The HFDU06 fingerprint scanner-based work here is displaying considerable efficiency which forms the image processing activities like image to binary and thinning for correcting and normalization of fingerprint patterns

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Garuba, Oluwatosin, I. M. Abdullahi, E. M. Dogo, and D. Maliki. "Investigating the Thresholding Effect and Fingerprint Transformation Using Cross-Correlation Similarity Matching." Proceedings of the Faculty of Science Conferences 1 (February 26, 2025): 25–29. https://doi.org/10.62050/fscp2024.440.

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This research presents a cross-correlation similarity matching method to study the fingerprint transformation and thresholding impact. This work directly compares the impact of various transformations (rotation, translation, elastic deformation, and scaling) on the fingerprint matching performance at different threshold values, in contrast to the standard minutiae-based systems. In order to compare the template positions of the two fingerprints using plots, the cross-correlation similarity matching of fingerprints first selects suitable templates in the primary fingerprint and then uses template matching to assess the impact of each transformation on matching accuracy, FRR, and FAR in the secondary print. The findings highlight the potential of thresholding in developing reliable and practical fingerprint recognition systems.

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Zhang, Jin Hai. "Research of Automatic Fingerprint Recognition Essential Algorithms." Applied Mechanics and Materials 135-136 (October 2011): 739–42. http://dx.doi.org/10.4028/www.scientific.net/amm.135-136.739.

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Fingerprint recognition has wide application prospect in all fields which contain identity authentication. Construction of accurate and reliable，safe and Practical automatic fingerprint identification system(AFIS) has become researc hotspot. Although theoretical research and application developmen of AFIS have got a significant Progress，accuracy of the algorithm and proeessing speeds till need to be improved. In this paper, fingerprint image preprocessing algorithms，fingerprint singular Points and minutiae extraction algorithm and fingerprint matching algorithm are analyzed and discussed in detail.

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Bakheet, Samy, Shtwai Alsubai, Abdullah Alqahtani, and Adel Binbusayyis. "Robust Fingerprint Minutiae Extraction and Matching Based on Improved SIFT Features." Applied Sciences 12, no. 12 (2022): 6122. http://dx.doi.org/10.3390/app12126122.

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Minutiae feature extraction and matching are not only two crucial tasks for identifying fingerprints, but also play an eminent role as core components of automated fingerprint recognition (AFR) systems, which first focus primarily on the identification and description of the salient minutiae points that impart individuality to each fingerprint and differentiate one fingerprint from another, and then matching their relative placement in a candidate fingerprint and previously stored fingerprint templates. In this paper, an automated minutiae extraction and matching framework is presented for identification and verification purposes, in which an adaptive scale-invariant feature transform (SIFT) detector is applied to high-contrast fingerprints preprocessed by means of denoising, binarization, thinning, dilation and enhancement to improve the quality of latent fingerprints. As a result, an optimized set of highly-reliable salient points discriminating fingerprint minutiae is identified and described accurately and quickly. Then, the SIFT descriptors of the local key-points in a given fingerprint are matched with those of the stored templates using a brute force algorithm, by assigning a score for each match based on the Euclidean distance between the SIFT descriptors of the two matched keypoints. Finally, a postprocessing dual-threshold filter is adaptively applied, which can potentially eliminate almost all the false matches, while discarding very few correct matches (less than 4%). The experimental evaluations on publicly available low-quality FVC2004 fingerprint datasets demonstrate that the proposed framework delivers comparable or superior performance to several state-of-the-art methods, achieving an average equal error rate (EER) value of 2.01%.

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Mahboob, Ferheen. "MINUTIA EXTRACTOR AND MINUTIA MATCHER BASED FINGER PRINT RECOGNITION USING DIGITAL IMAGE PROCESSING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31823.

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The MATLAB code presents a user-friendly graphical interface for fingerprint recognition and verification. Through this interface, users can load fingerprint images and execute a series of image processing tasks aimed at enhancing image quality and extracting crucial features. Operations such as histogram equalization, FFT enhancement, binarization, and thinning are available to optimize the fingerprint images for analysis. The system facilitates minutia extraction, identifying significant points like ridge endings and bifurcations, which are fundamental for fingerprint matching. Moreover, it includes functionalities for removing spurious minutiae, thereby improving the accuracy of the recognition process. Users can save and load fingerprint templates, streamlining the storage and retrieval of extracted features for matching purposes. Additionally, the system supports fingerprint matching, allowing users to compare templates and determine their similarity or match percentage. This versatile tool finds applications in biometric authentication systems, forensic analysis, and security domains, where robust fingerprint recognition and verification are imperative. Overall, the MATLAB code provides a comprehensive framework encapsulated within a user-friendly interface, offering researchers and practitioners a valuable resource for fingerprint analysis and biometric authentication. Keywords : Fingerprint recognition, GUI (Graphical User Interface), Security systems, Access control, Forensic analysis, User-friendly

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

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

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Zhang, Yu Jun, Miao Yu, Wan Tong Zhao, and Yang Xu. "Fast Gabor Filterbank and its Application in Fingerprint Texture Analysis." Advanced Materials Research 466-467 (February 2012): 1295–99. http://dx.doi.org/10.4028/www.scientific.net/amr.466-467.1295.

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Minutiae-based matching is the main method of fingerprint recognition. Anil K. Jain brought forward a method (FingerCode) which use Gabor filter to extract the texture feature of fingerprint and match it. In order to get a faster algorithm of fingerprint identification, the properties of the real part of Gabor filter are analyzed and the Gabor filter algorithm is accelerated in special conditions and is validated by experiment, which decreases the computational complexity from O(n2) to O(n).

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Bakheet, Samy, Ayoub Al-Hamadi, and Rehab Youssef. "A Fingerprint-Based Verification Framework Using Harris and SURF Feature Detection Algorithms." Applied Sciences 12, no. 4 (2022): 2028. http://dx.doi.org/10.3390/app12042028.

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Amongst all biometric-based personal authentication systems, a fingerprint that gives each person a unique identity is the most commonly used parameter for personal identification. In this paper, we present an automatic fingerprint-based authentication framework by means of fingerprint enhancement, feature extraction, and matching techniques. Initially, a variant of adaptive histogram equalization called CLAHE (contrast limited adaptive histogram equalization) along with a combination of FFT (fast Fourier transform), and Gabor filters are applied to enhance the contrast of fingerprint images. The fingerprint is then authenticated by picking a small amount of information from some local interest points called minutiae point features. These features are extracted from the thinned binary fingerprint image with a hybrid combination of Harris and SURF feature detectors to render significantly improved detection results. For fingerprint matching, the Euclidean distance between the corresponding Harris-SURF feature vectors of two feature points is used as a feature matching similarity measure of two fingerprint images. Moreover, an iterative algorithm called RANSAC (RANdom SAmple Consensus) is applied for fine matching and to automatically eliminate false matches and incorrect match points. Quantitative experimental results achieved on FVC2002 DB1 and FVC2000 DB1 public domain fingerprint databases demonstrate the good performance and feasibility of the proposed framework in terms of achieving average recognition rates of 95% and 92.5% for FVC2002 DB1 and FVC2000 DB1 databases, respectively.

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K., Krishna Prasad, and S. Aithal P. "A STUDY ON PRE AND POST PROCESSING OF FINGERPRINT THINNED IMAGE TO REMOVE SPURIOUS MINUTIAE FROM MINUTIAE TABLE." International Journal of Current Research and Modern Education 3, no. 1 (2018): 197–212. https://doi.org/10.5281/zenodo.1174543.

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In Fingerprint recognition, after the initial preprocessing, the feature is extracted from the Fingerprint thinned image. Extraction of crucial and beneficial capabilities or features of interest from a fingerprint image is an essential venture during recognition. Feature extraction algorithms pick handiest or only applicable features important for enhancing the performance of matching and recognition rate and outcomes with the feature vector. The feature extraction algorithms or techniques require only relevant features like minutiae details and do not require any background details or domain-specific details. They need to be smooth or easy to compute with a purpose to gain a viable or practicable technique for a huge image series. Minutiae details or fingerprint ridge ending or bifurcation details using skeletonized or thinning approach is a very popular method for feature extraction. The preprocessed thinned image is further post-processed to remove some false minutiae from minutiae table and which is generated through crossing number theory. One more purpose of post-processing is to reduce the number of minutiae points by removing false minutiae structures like spurs, ride breaks, short ridge, holes or islands, bridges, and ladders. In this paper w × w window neighborhood is considered for each minutia in Minutiae Table. Minutiae Table contains Ridge ending or bifurcation code as 1 or 3 with its location details means x and y position in two columns and the sum of these details as its fourth column. These Minutiae tables are used for generating Fingerprint Hash code, which can be used as index-or identity key in order to uniquely identify an individual person or as one factor in Multifactor Authentication Model.

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Bhairannawar, Satish S., K. B. Raja, and K. R. Venugopal. "An Efficient Reconfigurable Architecture for Fingerprint Recognition." VLSI Design 2016 (July 28, 2016): 1–22. http://dx.doi.org/10.1155/2016/9532762.

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The fingerprint identification is an efficient biometric technique to authenticate human beings in real-time Big Data Analytics. In this paper, we propose an efficient Finite State Machine (FSM) based reconfigurable architecture for fingerprint recognition. The fingerprint image is resized, and Compound Linear Binary Pattern (CLBP) is applied on fingerprint, followed by histogram to obtain histogram CLBP features. Discrete Wavelet Transform (DWT) Level 2 features are obtained by the same methodology. The novel matching score of CLBP is computed using histogram CLBP features of test image and fingerprint images in the database. Similarly, the DWT matching score is computed using DWT features of test image and fingerprint images in the database. Further, the matching scores of CLBP and DWT are fused with arithmetic equation using improvement factor. The performance parameters such as TSR (Total Success Rate), FAR (False Acceptance Rate), and FRR (False Rejection Rate) are computed using fusion scores with correlation matching technique for FVC2004 DB3 Database. The proposed fusion based VLSI architecture is synthesized on Virtex xc5vlx30T-3 FPGA board using Finite State Machine resulting in optimized parameters.

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Roopa, Ms, and Darshan Nayak M. "Blood Group Identification Using Fingerprint." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–7. https://doi.org/10.55041/ijsrem.spejss005.

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The fingerprint pattern stands out as the most authentic and unique characteristic defining human identity. This unique pattern is immutable and persists unaltered until an individual’s demise. In various circumstances, particularly in legal proceedings, fingerprint evidence is highly regarded. The distinctive minutiae pattern of each person is unparalleled, with the probability of resemblance being exceedingly low, nearly one in sixty-four thousand million. This distinctiveness holds true even for identical duplet. The individualistic ridge pattern persists unchanged from birth, serving as a constant aspect of personal identity. This paper presents a method involving the comparison of specific feature patterns derived from fingerprints for personal identification systems. Fingerprint data is employed in the investigation of blood group determination as well. In the process of fingerprint matching, ridge frequency is assessed, and spatial features are extracted using a Gabor filter for this specific purpose. Consequently, blood group determination can be performed using fingerprint analysis. Key Words: Fingerprint images, convolution neural networks, feature extraction, Convolutional neural networks (CNN).

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D.S, Suresh, V. Udayashankara, and Shantala C.P. "Fingerprint Recognition and matching using Minutiae Technique and Artificial Neural Networks." i-manager's Journal on Future Engineering and Technology 1, no. 4 (2006): 12–18. http://dx.doi.org/10.26634/jfet.1.4.983.

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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 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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K., Krishna Prasad. "A Conceptual Study on Image Enhancement Techniques for Fingerprint Images." International Journal of Applied Engineering and Management Letters (IJAEML) 1, no. 1 (2017): 63–72. https://doi.org/10.5281/zenodo.831678.

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Biometrics is an emerging field of research in recent years and has been devoted to the identification of individuals using one or more intrinsic physical or behavioral traits. Fingerprints are the prominent and widely acceptable biometric features compared to face, speech, iris, and other types of biometrics. Fingerprint characteristic or features are unique for everyone and which cannot change throughout the lifetime. Fingerprint biometrics is having applications in diverse fields like attendance system, criminology, mobile applications and logical access control system. This is the purpose behind the popularity of fingerprints as the biometric identifier. The biometric image captured through mobile supportive devices like the mobile camera or USB Fingerprint contains low-quality images. In fingerprint recognition system the quality of the image plays a very important role while matching two fingerprints. Most of the fingerprint recognition systems result in poor matching due to impurity or noisy images. So there is high necessity and scope for image preprocessing and enhancement techniques in order to improve the quality of fingerprint image and to obtain high accuracy in the matching process. In this paper, we discuss some approaches and methods for reducing noise or impurities and to improve the quality of the image before matching them. These techniques help the fingerprint recognition system to become robust and to obtain high quality in the matching process.

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Mradula, Jain, and Khurana Anshul. "Overview of Biometric Fingerprint Identification." International Journal of Trend in Scientific Research and Development 2, no. 3 (2018): 2721–25. https://doi.org/10.31142/ijtsrd14101.

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Fingerprint identification of a person is widely used in all over the world, as there are no two fingerprints like. Authentication and validation of an individual is done with the help of various factors like signature, user ids and passwords, palm, fingerprint, face, voice, heart beat, iris, etc. The fingerprint technique is advantageous for such recognition as compared to other techniques. This paper is a brief review in the field of fingerprint identification. The aim of this paper is to review various latest minutiae based, correlation based and other global, local methods for fingerprint matching and status of success of concurrent methods. Mradula Jain | Anshul Khurana "Overview of Biometric Fingerprint Identification" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: https://www.ijtsrd.com/papers/ijtsrd14101.pdf

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Yang, Leqian. "Advancements in Fingerprint Recognition Through Deep Learning: A Comprehensive Analysis of Novel Algorithms." Applied and Computational Engineering 105, no. 1 (2024): 1–8. http://dx.doi.org/10.54254/2755-2721/105/2024tj0054.

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Abstract. This paper presents a comprehensive exploration of the application of deep learning technology in fingerprint recognition, focusing on the development and impact of several novel algorithms. These algorithms have been specifically designed to address challenges in key sub-fields such as pose estimation, direction field estimation, minutiae extraction, and minutiae matching. By integrating deep learning techniques, these new approaches significantly enhance the accuracy, stability, and efficiency of fingerprint recognition systems. The study demonstrates that these algorithms surpass traditional methods in several critical areas, offering improved precision in recognizing fingerprints, particularly in high-noise environments. Furthermore, the fully differentiable nature of these models contributes to their robustness, enabling more consistent and reliable performance across diverse scenarios. The results underscore the potential for these deep learning-based algorithms to set new benchmarks in the field, with broad implications for their application in security, law enforcement, and other areas requiring reliable biometric authentication. The use of these cutting-edge methods is anticipated to be vital in influencing the direction of fingerprint recognition technology as it develops further, guaranteeing increased security and precision.

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

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