Articoli di riviste: "Fingerprint Verification"

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Bercelin, Rose Mary W., N. Subbulakshmi, and Tofflin R. Bini. "Fingerprint Combination of Privacy Preservation is Improved by Delaunay Triangulation Method." Applied Mechanics and Materials 573 (June 2014): 437–41. http://dx.doi.org/10.4028/www.scientific.net/amm.573.437.

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Fingerprints are graphical ridge and valley patterns on the tips of human fingers. Owing to their uniqueness and permanence, the use of fingerprints is considered to be one of the most reliable methods of personal verification. Fingerprints are today among the most popularly used biometric modalities in automatic verification systems. At the same time, Protecting the privacy of Fingerprint is an important one, here a novel system propose protecting fingerprint by combining two different fingerprints. In the enrolment stage, fingerprints will be captured from two different fingers. Minutiae positions are extracted from one fingerprint (its the detailed pattern of the fingerprint). The orientation, (it indicates the flow of ridges and valleys) from other fingerprint, and the reference points are calculated from both the two fingerprints. Extracted combined minutiae templates are stored into database. Here A Delaunay Triangulation indexing scheme has been designed for protecting biometric template and non-invertible template are calculated for verifies the authorized person. By using the Delaunay triangulation method fingerprint privacy has to be improved.

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Melyani Melyani. "Test Data Algoritma Kromosom Pada Sidik Jari Menggunakan Jaringan Syaraf." JURNAL PENELITIAN SISTEM INFORMASI (JPSI) 2, no. 1 (January 27, 2024): 138–62. http://dx.doi.org/10.54066/jpsi.v2i1.1550.

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Biometric features that can be used for identification include iris, voice, DNA and fingerprints. Fingerprints are the most widely used biometric feature because of their uniqueness, universality and stability. Fingerprint recognition can be grouped into two different forms of problems, namely verification and identification. Verification is comparing one fingerprint with another fingerprint. Meanwhile, identification is matching an input fingerprint with fingerprint data in the database. Thus, identification can be interpreted as an extension of verification carried out by comparing one fingerprint to many fingerprints. Identification is inherently more complex than verification. The problem increases as the number of fingerprint datasets increases, resulting in an increase in the time required for the identification process. However, there is a way to overcome this complexity, namely classification. Apart from that, evolutionary algorithm optimization can also be carried out. The Chromosome Algorithm is an improvement on the evolutionary algorithm with a separate local search process. The memetic or chromosomal algorithm is a simple algorithm with reliable performance that can provide accurate solutions to problems in the real world. The current challenge is with the increasing growth of datasets (more than 106) which include the process of clustering text analysis, molecular DNA simulation, feature selection, and forecasting, handling large-scale optimization such as complex simulations, data mining, quantum chemistry, spectroscopic analysis, geophysical analysis, drug discovery, and fingerprint recognition studies. Chromosome algorithms have proven to be very competitive in large-scale optimization because they are based on stochastic algorithms that do not require gradient information.

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Hemanth, Gannamani, Maddipati Vikas, Sai Nisarg D. Mehta, and Gaurav Varshney. "Fingerprint Voting System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (March 27, 2025): 1–9. https://doi.org/10.55041/ijsrem43165.

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Fingerprint voting systems offer a robust mechanism for ensuring secure and reliable electoral processes. This paper proposes a comprehensive approach to government verification and voter authentication within fingerprint voting systems. Beginning with the initial voter registration process, government authorities authenticate individuals by capturing their fingerprints and verifying their identities against national databases. Upon successful verification, voters are enrolled in the system and provided with unique biometric identifiers. During elections, voters undergo authentication using their registered fingerprints to access the voting interface. The system matches the presented fingerprint with the stored template to confirm the identity of the voter. Once authenticated, voters proceed to cast their ballots electronically. Throughout this process, stringent security measures are implemented to safeguard against unauthorized access and fraudulent activities. Furthermore, the paper discusses the integration of government verification procedures into the voting system architecture, ensuring compliance with legal requirements and electoral regulations. Additionally, considerations such as data privacy, integrity, and accessibility are addressed to guarantee the inclusivity and transparency of the voting process. By adopting this comprehensive approach, fingerprint voting systems can enhance the efficiency, integrity, and trustworthiness of electoral procedures, thereby promoting democratic values and ensuring the legitimacy of election outcomes. Fingerprint voting systems have emerged as a promising solution for ensuring the security and integrity of electoral processes. This paper presents a comprehensive approach to government verification and voter authentication within fingerprint- based voting systems, aiming to establish a robust framework for secure and reliable elections. The proposed approach begins with the initial voter registration process, wherein government authorities verify the identities of individuals seeking to participate in elections. During registration, individuals’ fingerprints are captured and authenticated against national databases to ensure their eligibility to vote. Upon successful verification, voters are enrolled in the system and assigned unique biometric identifiers, facilitating their authentication during subsequent electoral events. During elections, voters are required to authenticate themselves using their registered fingerprints before accessing the voting interface. This authentication process involves matching the presented fingerprint with the stored template to confirm the identity of the voter. Only authenticated voters are granted access to cast their ballots electronically, thereby safeguarding the integrity of the voting process.

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Priati, Assiroj, L. H. S. Warnars H., Abdurrachman E., I. Kistijantoro A., and Doucet A. "Measuring memetic algorithm performance on image fingerprints dataset." TELKOMNIKA Telecommunication, Computing, Electronics and Control 19, no. 1 (February 1, 2021): pp. 96~104. https://doi.org/10.12928/TELKOMNIKA.v19i1.16418.

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Personal identification has become one of the most important terms in our society regarding access control, crime and forensic identification, banking and also computer system. The fingerprint is the most used biometric feature caused by its unique, universality and stability. The fingerprint is widely used as a security feature for forensic recognition, building access, automatic teller machine (ATM) authentication or payment. Fingerprint recognition could be grouped in two various forms, verification and identification. Verification compares one on one fingerprint data. Identification is matching input fingerprint with data that saved in the database. In this paper, we measure the performance of the memetic algorithm to process the image fingerprints dataset. Before we run this algorithm, we divide our fingerprints into four groups according to its characteristics and make 15 specimens of data, do four partial tests and at the last of work we measure all computation time.  

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QIN, WEI, and YILONG YIN. "A NOVEL METHOD USING VIDEOS FOR FINGERPRINT VERIFICATION." International Journal of Pattern Recognition and Artificial Intelligence 26, no. 01 (February 2012): 1256002. http://dx.doi.org/10.1142/s0218001412560022.

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Traditional fingerprint verifications use single image for matching. However, the verification accuracy cannot meet the need of some application domains. In this paper, we propose to use videos for fingerprint verification. To take full use of the information contained in fingerprint videos, we present a novel method to use the dynamic as well as the static information in fingerprint videos. After preprocessing and aligning processes, the Inclusion Ratio of two matching fingerprint videos is calculated and used to represent the similarity between these two videos. Experimental results show that video-based method can access better accuracy than the method based on single fingerprint.

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

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

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J, Nandhakumar. "IoT Based Voting Machine with Fingerprint Verification." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 1490–96. http://dx.doi.org/10.22214/ijraset.2022.42573.

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Abstract: In a democratic country, like India voting is an important way where the citizen can cast their vote. Usually voting is done by casting their vote in polling booth. As the technology increases, nowadays electronic voting machine is used for casting vote. This paper is about an IoT based voting machine with fingerprint verification. The main aim of this project is to make voting secure using fingerprint verification and also to reduce malpractices. The details of the voter along with their fingerprint in stored in database. If the fingerprint matches with the stored fingerprint, the system checks the aadhar number of the user and if authenticated, it checks if multiple votes have been cast. If the fingerprint matching is not correct “Matching failed” message will be displayed and if aadhar number is not correct, then “Aadhar not match” message will be displayed. Voter can enter his/her native place and vote for the corresponding candidate using thingspeak and the result can be obtained using the same. The Arduino Uno is the controller used in this project. Fingerprint is used to authenticate the user. There is at least a slight difference between the fingerprints of each person. When a malpractice occurs, “Already voted”message will be displayed. The arduino IDE is used for programming the board and cloud is used to display ballot card and to store the result. System provides an alert on malpractice and only an authorized voter can cast the vote. This project safeguards the citizen’s right to vote and guarantee fair election.

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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 (April 22, 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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Pan, Ming-Sie, Chao-Hsing Fan, Ching-Chao Yang, Sheng-Gui Su, Yu-Hsin Cheng, Nai-Chieh Wei, and Hsiang-Chen Hsu. "Fingerprint preprocessing using FCN and U-net methods." Journal of Information and Optimization Sciences 45, no. 5 (2024): 1421–34. http://dx.doi.org/10.47974/jios-1731.

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This paper analyzes the importance of optimizing fingerprint images from the perspective of front-line forensics personnel. Due to defects such as fingerprint ridge blurring and fingerprint image overlap, it may cause delays in fingerprint identification and lead to misjudgments (false positives, false negatives) and other consequences. This paper simulated common fingerprint images (fuzzy feature points) at criminal cases, and captured, segmented, and reconstructed fingerprints. Two deep learning model architectures, U-net [1] and FCN [2], were applied to realize image segmentation and image reconstruction. The first task is fingerprint image segmentation, where the fused images are segmented into incomplete fingerprints. The second task is to reconstruct the complete fingerprint from the image obtained after segmenting the incomplete fingerprint. Field verification for fingerprint image reconstruction and separation on red envelope were carried out. Ethyl cyanoacrylate method is used to visualize paper latent fingerprints and would produce “white” fingerprints, which is not easily distinguished from the red background. The pre-processing of fingerprints on red paper background is the key to the accuracy of fingerprint recognition. U-net image processing technology demonstrates the contributions of the forensic personnel in processing fingerprint images are as follows: [1] Improve efficiency. [2] Reduce misjudgment.

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Upendra, K., S. Singh, V. Kumar, and H. K. Verma. "Online fingerprint verification." Journal of Medical Engineering & Technology 31, no. 1 (January 2007): 36–45. http://dx.doi.org/10.1080/03091900500324194.

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â€˜Aisyah Saâ€™dan, Siti, Rajeswari Raju, Nursuriati Jamil, and . "Clonal Selection Algorithm for Low Quality Fingerprint Image Verification." International Journal of Engineering & Technology 7, no. 4.42 (December 29, 2018): 157–60. http://dx.doi.org/10.14419/ijet.v7i4.42.25702.

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Fingerprint verification has drawn a lot of attention to its approach in biometric since it is one of the most important biometric technologies nowadays and is widely used in several different applications and areas. It is applied in the forensic science area in order to identify people who were involved in criminal scenes such as the victims and the suspects. A humanâ€™s fingerprint is unique and usually has its own patterns and ridges, which differs them from otherâ€™s fingerprints. However, there are some drawbacks that can cause low accuracy and low performance of the verification. This occurs when the fingerprint images used are of low-quality and the fingerprints may be slightly incomplete (partial). Clonal Selection Algorithm (CSA) is known to be good in pattern matching and optimization of problems. Hence, this paper discusses the finding of the implementation of CSA in fingerprint verification. There were two main processes involved, which are features extraction using minutiae-based method and also the implementation of the CSA algorithm. Study shows that the FNMR result is 33.33% and the FMR is 16.67%. Further studies can be carried out by using the same algorithm, but focusing more on the feature extraction methods to improve the extraction of fingerprints.Â Â

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Sravani, P. S. L. "Robust Fingerprint Verification-Using Real Time Security Alerts." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (March 31, 2025): 1753–59. https://doi.org/10.22214/ijraset.2025.67644.

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This work presents Robust Fingerprint Authentication with Real-Time Security Alerts, a biometric authentication system that integrates Aadhaar-based identity verification, liveness detection, and real-time SMS notifications. Traditional fingerprint authentication systems store biometric data persistently, increasing the risk of breaches and unauthorized access. To address this, the proposed system ensures immediate deletion of fingerprint data after verification, enhancing security and user privacy. Advanced image processing techniques and deep learning models are employed for liveness detection, mitigating spoofing attacks using fake fingerprints. An OTP-based authentication mechanism adds an additional security layer, while real-time SMS notifications inform users of authentication attempts, improving transparency. The system is implemented using Flask, PostgreSQL, OpenCV, Twilio, and TensorFlow, ensuring scalability, efficiency, and secure data handling. HTTPS encryption and compliance with GDPR and CCPA regulations further strengthen security by enforcing responsible biometric data processing. Experimental results demonstrate 98.5% fingerprint matching accuracy, 97.8% liveness detection success, and 99.3% OTP verification accuracy, surpassing traditional fingerprint authentication models. By integrating multi-layered security features and real-time user alerts, this system provides a privacy-conscious, secure, and efficient biometric authentication solution suitable for applications in banking, government services, and enterprise security

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Almehmadi, Abdulaziz. "A Behavioral-Based Fingerprint Liveness and Willingness Detection System." Applied Sciences 12, no. 22 (November 11, 2022): 11460. http://dx.doi.org/10.3390/app122211460.

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Fingerprints have been used for decades to verify the identity of an individual for various security reasons. Attackers have developed many approaches to deceive a fingerprint verification system, ranging from the sensor level, where gummy fingers are created, to gaining access to the decision-maker level, where the decision is made based on low matching criteria. Even though fingerprint sensor-level countermeasures have developed advanced metrics to detect any attempt to dupe the system, attackers still manage to outwit a fingerprint verification system. In this paper, we present the Micro-behavioral Fingerprint Analysis System (MFAS), a system that records the micro-behavior of the user’s fingertips over time as they are placing their fingerprint on the sensor. The system captures the stream of ridges as they are formed while placed on a sensor to combat the attacks that deceive the sensor. An experiment on 24 people was conducted, wherein the fingerprints and the behavior of the fingertip as it is placed were collected. Subsequently, a gummy finger was created to try to fool the system. Further, a legitimate user was chosen to participate in an experiment that mimicked an attempt to use their fingertip unwillingly to detect coerced fingerprint placement. After applying the micro-behavior, the system reported 100% true positives and 0% false-negatives when providing legitimate vs. gummy-based fingerprints to authenticate a malicious user. The system also reported a 100% accuracy in differentiating between a voluntary and a coerced fingerprint placement. The results improve the fingerprint robustness against attacks on a fingerprint sensor by factoring in micro-behavior, thus helping to overcome fake and coerced fingerprint attacks.

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Khan, Sajid, Dong-Ho Lee, Asif Khan, Ahmad Waqas, Abdul Rehman Gilal, and Zahid Hussain Khand. "A Digital Camera-Based Rotation-Invariant Fingerprint Verification Method." Scientific Programming 2020 (May 15, 2020): 1–10. http://dx.doi.org/10.1155/2020/9758049.

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Fingerprint registration and verification is an active area of research in the field of image processing. Usually, fingerprints are obtained from sensors; however, there is recent interest in using images of fingers obtained from digital cameras instead of scanners. An unaddressed issue in the processing of fingerprints extracted from digital images is the angle of the finger during image capture. To match a fingerprint with 100% accuracy, the angles of the matching features should be similar. This paper proposes a rotation and scale-invariant decision-making method for the intelligent registration and recognition of fingerprints. A digital image of a finger is taken as the input and compared with a reference image for derotation. Derotation is performed by applying binary segmentation on both images, followed by the application of speeded up robust feature (SURF) extraction and then feature matching. Potential inliers are extracted from matched features by applying the M-estimator. Matched inlier points are used to form a homography matrix, the difference in the rotation angles of the finger in both the input and reference images is calculated, and finally, derotation is performed. Input fingerprint features are extracted and compared or stored based on the decision support system required for the situation.

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Yogita, Verma* Prof. Bhagwati Charan Patel. "FINGERPRINT DETECTION AND RECOGNIZATION TECHNIQUES USING GABOR FILTER." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 5 (May 31, 2017): 722–29. https://doi.org/10.5281/zenodo.801293.

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Fingerprints are most extensively and effectively appropriate for the proof of identity in present days. Mostly because of their uniqueness among the people, public acceptance, originality, stability through life, and their least risk of invasion. Fingerprint technology, which is basically a biometric system, is utilized to identify an individual based on their physical qualities. Fingerprint matching is the trendiest biometric method appropriate to provide authentication. Fingerprint verification is one of the most trustable biometric security system in the world of computers. In this paper we proposed fingerprint algorithm which uses Gabor filter to capture local and global minute details in a fingerprint. The matching is based Euclidean distance between input image to test compared to trained fingerprints images. We are able to detect fingerprint with marginally best results. Our fingerprint framework performs best as compared to any other techniques.

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Odu, Tiwalade O., Moses O. Olaniyan, Tokunbo Ogunfunmi, Isaac A. Samuel, Joke A. Badejo, and Aderemi A. Atayero. "Multi-Instance Contingent Fusion for the Verification of Infant Fingerprints." Journal of Electrical and Computer Engineering 2024 (January 2, 2024): 1–10. http://dx.doi.org/10.1155/2024/7728707.

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It is imperative to establish an automated system for the identification of neonates (1–28 days old) and infants (29 days–12 months old) through the utilisation of the readily accessible 500 ppi fingerprint reader. This measure is crucial in addressing the issue of newborn swapping, facilitating the identification of missing children, monitoring immunisation records, maintaining comprehensive medical history, and other related purposes. The objective of this study is to demonstrate the potential for future identification of infants using fingerprints obtained from a 500 ppi fingerprint reader by employing a fusion technique that combines multiple instances of fingerprints, specifically the left thumb and right index fingers. The fingerprints were acquired from babies who were between the ages of one day and six months at the enrolment session. The sum-score fusion algorithm was implemented. The approach mentioned above yielded verification accuracies of 73.8%, 69.05%, and 57.14% for time intervals of 1 month, 3 months, and 6 months, respectively, between the enrolment and query fingerprints.

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Wang, Feng. "Fusion Fingerprint and Face Multi-Biometrics Recognition Based on D-S Evidence Theory." Advanced Materials Research 459 (January 2012): 644–48. http://dx.doi.org/10.4028/www.scientific.net/amr.459.644.

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Single biometric feature has not meet people's needs. After analyzing fingerprints and face recognition technology, a fused theory is proposed which comprise fingerprints and face multi-biometrics features recognition based on D-S evidence theory. This article first analysis main part of the Face and Fingerprint Identification System, then gives a decision-making on integration in the face and fingerprint recognition method. In this paper we divide minutia features of fingerprint into certain and uncertain region which could make the performance of verification in certain region better than the original performance. By this fusion strategy the whole performance is improved.

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Kumar, Ravinder, Pravin Chandra, and M. Hanmandlu. "Fingerprint Matching Based on Orientation Feature." Advanced Materials Research 403-408 (November 2011): 888–94. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.888.

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This paper presents a fast and reliable algorithm for fingerprint verification. Our proposed fingerprint verification algorithm is based on image-based fingerprint matching. The improved orientation feature vector of two fingerprints has been compared to compute the similarities at a given threshold. Fingerprint image has been aligned by rotating through an angle before feature vector is computed and matched. Row and Column variance feature vector of orientation image will be employed. The algorithm has been tested on the FVC2002 Databases. The performance of algorithm is measured in terms of GAR and FAR. At a threshold level of 1.1 % and at 5.7% FAR the GAR observed is 97.83%. The improved Feature vector will lower imposter acceptance rate at reasonable GAR and hence yields better GAR at lower FAR. The proposed algorithm is computationally very efficient and can be implemented on Real-Time Systems.

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Kirvel, Vitali. "Fingerprint analysis (AFIS) and biometric system of identification by fingerprints: issue of artificial papillary pictures." Общество и инновации 1, no. 3 (January 1, 2021): 129–45. http://dx.doi.org/10.47689/2181-1415-vol1-iss3-pp129-145.

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The article discusses the theoretical and applied aspects, as well as prospects for the development of fingerprint examination, fingerprint records (automated fingerprint identification systems-AFIS) in the disclosure and uncovering of crimes, as well as biometric identification systems by fingerprints to protect personal data, control access to corporate and personal information, time tracking in the information sector of the economy. The paper presents methods form an ufacturing models with artificial papillary patterns (falsification of papillary patterns of fingerprints), the results of experiments that consisted in creating a model (dummy) of the nail phalanx of the finger with an artificial papillary pattern and verification of a biometric scanner using biometric technologies.

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Jiang, Yujia, and Xin Liu. "Uniform Local Binary Pattern for Fingerprint Liveness Detection in the Gaussian Pyramid." Journal of Electrical and Computer Engineering 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/1539298.

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Fingerprint recognition schemas are widely used in our daily life, such as Door Security, Identification, and Phone Verification. However, the existing problem is that fingerprint recognition systems are easily tricked by fake fingerprints for collaboration. Therefore, designing a fingerprint liveness detection module in fingerprint recognition systems is necessary. To solve the above problem and discriminate true fingerprint from fake ones, a novel software-based liveness detection approach using uniform local binary pattern (ULBP) in spatial pyramid is applied to recognize fingerprint liveness in this paper. Firstly, preprocessing operation for each fingerprint is necessary. Then, to solve image rotation and scale invariance, three-layer spatial pyramids of fingerprints are introduced in this paper. Next, texture information for three layers spatial pyramids is described by using uniform local binary pattern to extract features of given fingerprints. The accuracy of our proposed method has been compared with several state-of-the-art methods in fingerprint liveness detection. Experiments based on standard databases, taken from Liveness Detection Competition 2013 composed of four different fingerprint sensors, have been carried out. Finally, classifier model based on extracted features is trained using SVM classifier. Experimental results present that our proposed method can achieve high recognition accuracy compared with other methods.

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Praseetha, V. M., Saad Bayezeed, and S. Vadivel. "Secure Fingerprint Authentication Using Deep Learning and Minutiae Verification." Journal of Intelligent Systems 29, no. 1 (April 10, 2019): 1379–87. http://dx.doi.org/10.1515/jisys-2018-0289.

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Abstract Nowadays, there has been an increase in security concerns regarding fingerprint biometrics. This problem arises due to technological advancements in bypassing and hacking methodologies. This has sparked the need for a more secure platform for identification. In this paper, we have used a deep Convolutional Neural Network as a pre-verification filter to filter out bad or malicious fingerprints. As deep learning allows the system to be more accurate at detecting and reducing false identification by training itself again and again with test samples, the proposed method improves the security and accuracy by multiple folds. The implementation of a novel secure fingerprint verification platform that takes the optical image of a fingerprint as input is explained in this paper. The given input is pre-verified using Google’s pre-trained inception model for deep learning applications, and then passed through a minutia-based algorithm for user authentication. Then, the results are compared with existing models.

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Hebaishy, Mohamed A., and Fathy A. Syam. "Fast Fingerprint Identification based on the DoG Filter." WSEAS TRANSACTIONS ON SIGNAL PROCESSING 21 (March 31, 2025): 19–24. https://doi.org/10.37394/232014.2025.21.3.

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Biometric recognition is the study of recognizing individuals through their distinct physiological or behavioral traits, which encompass iris, facial features, fingerprints, retina patterns, vein structures, hand geometry, handwriting, human gait, signatures, keystrokes, and vocal characteristics. Fingerprint images comprise various whorls, arches, loops, ridge ends, and ridge bifurcations on each digit. The extraction of these features with some transformations can uniquely identify an individual. Fingerprints are extensively utilized in numerous biometric identification systems; finger scanning is efficient, user-friendly, and a well-established technique. This study examines the extraction of the fingerprint features and enhances the efficacy of the fingerprint authentication system by implementing a novel extractor for fingerprint features utilizing the DoG(Difference of Gaussian) Filter. The extracted features are encoded and prepared for the matching process to get the system identification and verification results.

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Jain, A., Lin Hong, and R. Bolle. "On-line fingerprint verification." IEEE Transactions on Pattern Analysis and Machine Intelligence 19, no. 4 (April 1997): 302–14. http://dx.doi.org/10.1109/34.587996.

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Maio, D., D. Maltoni, R. Cappelli, J. L. Wayman, and A. K. Jain. "FVC2000: fingerprint verification competition." IEEE Transactions on Pattern Analysis and Machine Intelligence 24, no. 3 (March 2002): 402–12. http://dx.doi.org/10.1109/34.990140.

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Cappelli, Raffaele, Matteo Ferrara, Annalisa Franco, and Davide Maltoni. "Fingerprint verification competition 2006." Biometric Technology Today 15, no. 7-8 (July 2007): 7–9. http://dx.doi.org/10.1016/s0969-4765(07)70140-6.

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Mesec, Chip. "Fingerprint identification versus verification." Biometric Technology Today 15, no. 9 (September 2007): 7. http://dx.doi.org/10.1016/s0969-4765(07)70157-1.

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Qin, Wei, Yilong Yin, and Lili Liu. "Video-Based Fingerprint Verification." Sensors 13, no. 9 (September 4, 2013): 11660–86. http://dx.doi.org/10.3390/s130911660.

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Ziyatbekova, G. Z., and M. A. Shaikhy. "Biometric technology for personal identification and authentication: fingerprint." Bulletin of the National Engineering Academy of the Republic of Kazakhstan 86, no. 4 (December 30, 2022): 62–70. http://dx.doi.org/10.47533/2020.1606-146x.196.

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Abstract (sommario):

This paper presents an efficient fingerprint verification algorithm that improves matching accuracy. Algorithms for image processing for biometric technologies of identification and authentication of a person using fingerprints are considered. They are essential tools for improving performance across a wide range of industries. The recognition system is designed for storing data, further processing it, identifying and displaying fingerprints. In forensics, modern image processing algorithms are used and provide a significant improvement in work results. Fingerprint images are degraded and damaged due to changes in skin and print conditions. Thus, image enhancement techniques are used before feature points are detected and fine details are extracted. Templates are generated based on feature vector extraction and databases are created for verification and identification of fingerprint images. The method of experimental research is carried out, the process of processing the identification results is considered. To solve this problem, a recognition system was proposed, which is designed to store data, further process it, identify and display fingerprints. They are used in places of high secrecy as a kind of password access key based on scanning and verifying fingerprints with the FVC2002 database. The results obtained show that the proposed algorithm is more efficient and reliable.

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Akinribido, C.T. "Cryptographic Security Approach for Biometric Verification System." Indian Journal of Cryptography and Network Security (IJCNS) 3, no. 2 (December 30, 2023): 7–13. https://doi.org/10.54105/ijcns.C7854.113223.

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<strong>Abstract: </strong>This paper presents cryptography which is the science of encryption and decryption to protect fingerprint that can be transmitted from sender to receiver. This security approach (cryptography) can also be applicable to other biometric traits like face, iris, retina and palm print. The significant of this protective medium is to prevent intruders or attacker to easily perceive the presence of fingerprint images. The method that was utilized for implementation of this cryptographic security approach for fingerprint verification System was achieved through Elliptic Curve Cryptography and Hill Cipher Algorithm. An elliptic Curve Function was defined and its domain parameters were used to generate self invertible key matrix that was used for the encryption and decryption process of the biometric images (Fingerprint, Face and Ear ) The security approach was also improved by ensuring that the decryption can only be done through secret key. After decryption, fingerprint verification was done by extracting and matching distinct feature (ridges) from template fingerprint and distinct feature from input fingerprint. The result obtained from demonstration of the cryptographic approach allows end users to load fingerprint image, encrypt it at sending end. At receiver end, end user can decrypt the fingerprint image. Image enhancement was later done before authentication through extraction and matching of distinct features. This model will prevent destruction and manipulation of stored fingerprint image. Authentication can also be accomplished through biological traits instead of use of password that can be stolen or transferred to relative.

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Chan, K. C., Y. S. Moon, and P. S. Cheng. "Fast Fingerprint Verification Using Subregions of Fingerprint Images." IEEE Transactions on Circuits and Systems for Video Technology 14, no. 1 (January 2004): 95–101. http://dx.doi.org/10.1109/tcsvt.2003.818358.

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31

GONZÁLEZ, MARIO, DAVID DOMINGUEZ, FRANCISCO B. RODRÍGUEZ, and ÁNGEL SÁNCHEZ. "RETRIEVAL OF NOISY FINGERPRINT PATTERNS USING METRIC ATTRACTOR NETWORKS." International Journal of Neural Systems 24, no. 07 (October 10, 2014): 1450025. http://dx.doi.org/10.1142/s0129065714500257.

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Abstract (sommario):

This work experimentally analyzes the learning and retrieval capabilities of the diluted metric attractor neural network when applied to collections of fingerprint images. The computational cost of the network decreases with the dilution, so we can increase the region of interest to cover almost the complete fingerprint. The network retrieval was successfully tested for different noisy configurations of the fingerprints, and proved to be robust with a large basin of attraction. We showed that network topologies with a 2D-Grid arrangement adapt better to the fingerprints spatial structure, outperforming the typical 1D-Ring configuration. An optimal ratio of local connections to random shortcuts that better represent the intrinsic spatial structure of the fingerprints was found, and its influence on the retrieval quality was characterized in a phase diagram. Since the present model is a set of nonlinear equations, it is possible to go beyond the naïve static solution (consisting in matching two fingerprints using a fixed distance threshold value), and a crossing evolution of similarities was shown, leading to the retrieval of the right fingerprint from an apparently more distant candidate. This feature could be very useful for fingerprint verification to discriminate between fingerprints pairs.

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Nwe, Ni Kyaw, Kyaw Naing Kyaw, and Myo Nwe Wai Myat. "Identification of Persons by Fingerprint using Huffman Coding Algorithm." International Journal of Trend in Scientific Research and Development 3, no. 5 (July 17, 2019): 1208–11. https://doi.org/10.5281/zenodo.3590611.

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Abstract (sommario):

Fingerprint system is one of the most common biometric features used for personal identification and verification. There are many techniques used for finding the features of fingerprint when it matches the other images of fingerprint. This system must input the fingerprint for the corresponding profile. It converts the binary image. This image will convert the Huffman code approach and to generate codes for input fingerprint. The system has the profile information along with their associated fingerprints. When the system identified the fingerprint, the system will display the associated profile information for the corresponding image. Huffman compression technique was the most accurate technique for compression of fingerprint image. Nwe Ni Kyaw | Kyaw Kyaw Naing | Myat Myo Nwe Wai "Identification of Persons by Fingerprint using Huffman Coding Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26600.pdf

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Bhatti, Kamran Ali, Dr Sajida Parveen, Nadeem Naeem Bhatti, Kamran Ali Larik, and Qurat-ul-ain Soomro. "Fingerprint liveness detection using dynamic local ternary pattern (DLTP)." VFAST Transactions on Software Engineering 12, no. 2 (June 28, 2024): 123–31. http://dx.doi.org/10.21015/vtse.v12i2.1842.

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Abstract (sommario):

Nowadays, biometric confirmation systems are utilized for security applications such as verification and identification. There are various biometric modalities such as fingerprints, face recognition, and iris scans. Biometric systems are superior to PIN and password-based systems because the latter can be easily stolen or forgotten, whereas biometric traits are unique and difficult to replicate or forget. Among biometric modalities, fingerprint recognition is widely employed for security purposes due to the distinctiveness of each individual's fingerprint. However, fingerprint biometric systems encounter challenges, such as the reproduction of fake fingerprints using materials like silicon, which can potentially bypass security measures. This issue is considered a significant problem in fingerprint systems.This paper proposes a new software-based method called Dynamic Local Ternary Pattern (DLTP) for fingerprint liveness detection, employing a machine learning classifier, specifically Support Vector Machine (SVM), to distinguish between live and fake fingerprints. Various experiments were conducted using DLTP and state-of-the-art texture descriptors. The results obtained from DLTP demonstrated optimal accuracy, clearly surpassing those achieved by the Local Binary Pattern (LBP) and Local Ternary Pattern (LTP) texture descriptors reported in previous studies.

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Le, Ngoc Tuyen, Duc Huy Le, Jing-Wein Wang, and Chih-Chiang Wang. "Entropy-Based Clustering Algorithm for Fingerprint Singular Point Detection." Entropy 21, no. 8 (August 12, 2019): 786. http://dx.doi.org/10.3390/e21080786.

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Abstract (sommario):

Fingerprints have long been used in automated fingerprint identification or verification systems. Singular points (SPs), namely the core and delta point, are the basic features widely used for fingerprint registration, orientation field estimation, and fingerprint classification. In this study, we propose an adaptive method to detect SPs in a fingerprint image. The algorithm consists of three stages. First, an innovative enhancement method based on singular value decomposition is applied to remove the background of the fingerprint image. Second, a blurring detection and boundary segmentation algorithm based on the innovative image enhancement is proposed to detect the region of impression. Finally, an adaptive method based on wavelet extrema and the Henry system for core point detection is proposed. Experiments conducted using the FVC2002 DB1 and DB2 databases prove that our method can detect SPs reliably.

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35

C.T, Akinribido, Olabode O.O, Adetunmbi O.A, and Adewumi Moradeke Grace. "Cryptographic Security Approach for Biometric Verification System." Indian Journal of Cryptography and Network Security 3, no. 2 (December 30, 2023): 7–13. http://dx.doi.org/10.54105/ijcns.c7854.113223.

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Abstract (sommario):

This paper presents cryptography which is the science of encryption and decryption to protect fingerprint that can be transmitted from sender to receiver. This security approach (cryptography) can also be applicable to other biometric traits like face, iris, retina and palm print. The significant of this protective medium is to prevent intruders or attacker to easily perceive the presence of fingerprint images. The method that was utilized for implementation of this cryptographic security approach for fingerprint verification System was achieved through Elliptic Curve Cryptography and Hill Cipher Algorithm. An elliptic Curve Function was defined and its domain parameters were used to generate self invertible key matrix that was used for the encryption and decryption process of the biometric images (Fingerprint, Face and Ear ) The security approach was also improved by ensuring that the decryption can only be done through secret key. After decryption, fingerprint verification was done by extracting and matching distinct feature (ridges) from template fingerprint and distinct feature from input fingerprint. The result obtained from demonstration of the cryptographic approach allows end users to load fingerprint image, encrypt it at sending end. At receiver end, end user can decrypt the fingerprint image. Image enhancement was later done before authentication through extraction and matching of distinct features. This model will prevent destruction and manipulation of stored fingerprint image. Authentication can also be accomplished through biological traits instead of use of password that can be stolen or transferred to relative.

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Lalovic, Komlen, Milan Milosavljevic, Ivan Tot, and Nemanja Macek. "Device for biometric verification of maternity." Serbian Journal of Electrical Engineering 12, no. 3 (2015): 293–302. http://dx.doi.org/10.2298/sjee1503293l.

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Abstract (sommario):

Biometry is the scientific discipline and technology that measures and analyzes physiological or behavioral characteristics of people and is widely deployed in modern society security systems. Device for biometric identification of maternity is a dual fingerprint scanner that acquires fingerprint templates of the mother and the child at the very moment of birth, generates unique ID reference, and further guarantees mother-child relationship with that reference. Technical issue that is solved with this work and the proposed device is scanning, processing, and storing encrypted biometric templates with a goal to provide a 100% guarantee maternity for each new born child. Scanning the fingerprints of both mother and the child simultaneously, at moment of birth, and pairing them with unique ID reference removes potential fears occurring from hospital negligence to malicious activities, while the data encryption raises the whole process to the highest level of security and confidentiality. The main contribution of the device that removes the fear that almost every mother has in this period as it provides an answer to the question: ?Is this my baby?? with a 100% guarantee ?It?s certainly yours!?

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Jo, Young-Hoo, Seong-Yun Jeon, Jong-Hyuk Im, and Mun-Kyu Lee. "Security Analysis and Improvement of Fingerprint Authentication for Smartphones." Mobile Information Systems 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/8973828.

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Abstract (sommario):

Currently, an increasing number of smartphones are adopting fingerprint verification as a method to authenticate their users. Fingerprint verification is not only used to unlock these smartphones, but also used in financial applications such as online payment. Therefore, it is very crucial to secure the fingerprint verification mechanism for reliable services. In this paper, however, we identify a few vulnerabilities in one of the currently deployed smartphones equipped with fingerprint verification service by analyzing the service application. We demonstrate actual attacks via two proof-of-concept codes that exploit these vulnerabilities. By the first attack, a malicious application can obtain the fingerprint image of the owner of the victimized smartphone through message-based interprocess communication with the service application. In the second attack, an attacker can extract fingerprint features by decoding a file containing them in encrypted form. We also suggest a few possible countermeasures to prevent these attacks.

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Ramírez Flores, Manuel, Gualberto Aguilar Torres, Gina Gallegos García, and Miguel Ángel García Licona. "Fingerprint verification using computational geometry." DYNA 83, no. 195 (February 23, 2016): 128–37. http://dx.doi.org/10.15446/dyna.v83n195.46323.

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This paper presents a robust minutiae based method for fingerprint verification. The proposed method uses Delaunay Triangulation to represent minutiae as nodes of a connected graph composed of triangles. The minimum angle over all triangulations is maximized, which gives local stability to the constructed structures against rotation and translation variations. Geometric thresholds and minutiae data were used to characterize the triangulations created from input and template fingerprint images. The effectiveness of the proposed method is confirmed through calculations of false acceptance rate (FAR), false rejected rate (FRR) and equal error rate (EER) over FVC2002 databases compared to the results of other approaches.

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KIM, Bongil, Jaeyeon LEE, Chungkyue KIM, Kiyonari FUKUE, Haruhisa SHIMODA, and Toshifumi SAKATA. "Development of fingerprint verification system." Journal of the Japan society of photogrammetry and remote sensing 28, no. 1 (1989): 4–18. http://dx.doi.org/10.4287/jsprs.28.4.

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40

Gamble, F. T., L. M. Frye, and D. R. Grieser. "Real-time fingerprint verification system." Applied Optics 31, no. 5 (February 10, 1992): 652. http://dx.doi.org/10.1364/ao.31.000652.

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41

Khalil, Mohammed S., Dzulkifli Mohamad, Muhammad Khurram Khan, and Qais Al-Nuzaili. "Fingerprint verification using statistical descriptors." Digital Signal Processing 20, no. 4 (July 2010): 1264–73. http://dx.doi.org/10.1016/j.dsp.2009.12.002.

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42

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 (June 16, 2022): 6122. http://dx.doi.org/10.3390/app12126122.

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Abstract (sommario):

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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43

Srinivasa Raghavan, S., and G. Sivakumar. "Use of Fingerprint Recognition Systems in Libraries." Indian Journal of Information Sources and Services 2, no. 1 (May 5, 2012): 34–37. http://dx.doi.org/10.51983/ijiss.2012.2.1.348.

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This paper describes a comparative study on fingerprint recognition systems. The use of fingerprints for identification has been employed in law enforcement for about a century. A much broader application of fingerprints is for personal authentication, for instance to access a computer, a network, a bank-machine, a car, or a home. The following topics are covered: history, minutia types, image processing and methods, minutia extraction, enrollment and verification procedures, recognition rate, combination with other biometrics, and the future of fingerprint recognition system. AFIS is dramatically improving in the last decade. It can use in Library and Information Center for providing better security. The application of AFIS in Library and Information Center will protect from thief, mutilation of library materials and other unethical losses

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Yassin, Ali A. "Efficiency and Flexibility of Fingerprint Scheme Using Partial Encryption and Discrete Wavelet Transform to Verify User in Cloud Computing." International Scholarly Research Notices 2014 (September 24, 2014): 1–13. http://dx.doi.org/10.1155/2014/351696.

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Abstract (sommario):

Now, the security of digital images is considered more and more essential and fingerprint plays the main role in the world of image. Furthermore, fingerprint recognition is a scheme of biometric verification that applies pattern recognition techniques depending on image of fingerprint individually. In the cloud environment, an adversary has the ability to intercept information and must be secured from eavesdroppers. Unluckily, encryption and decryption functions are slow and they are often hard. Fingerprint techniques required extra hardware and software; it is masqueraded by artificial gummy fingers (spoof attacks). Additionally, when a large number of users are being verified at the same time, the mechanism will become slow. In this paper, we employed each of the partial encryptions of user’s fingerprint and discrete wavelet transform to obtain a new scheme of fingerprint verification. Moreover, our proposed scheme can overcome those problems; it does not require cost, reduces the computational supplies for huge volumes of fingerprint images, and resists well-known attacks. In addition, experimental results illustrate that our proposed scheme has a good performance of user’s fingerprint verification.

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Chandrakant P. Divate. "Design of Fingerprint-Based Gender Clustering Using Fuzzy C-Means Algorithm." Journal of Electrical Systems 20, no. 3s (April 4, 2024): 1046–62. http://dx.doi.org/10.52783/jes.1419.

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Biometric systems, with fingerprint recognition as a cornerstone, play a crucial role in identity verification and access control. This paper introduces an innovative approach to gender clustering based on fingerprint data, leveraging the Fuzzy C-Means (FCM) algorithm. The proposed design aims to enhance the precision of gender classification by incorporating the inherent uncertainty in fingerprint features. Through the integration of FCM, which allows soft assignment of fingerprints to gender clusters, the system achieves a nuanced and adaptable classification. The study meticulously explores the design considerations, including the selection of features, data preprocessing, and the configuration of FCM parameters. Additionally, the paper discusses the experimental results, demonstrating the effectiveness of the proposed method in accurately clustering fingerprint data according to gender. The research contributes to the evolving field of biometrics, offering a novel perspective on gender classification that embraces the complexity of fingerprint patterns. The outcomes provide valuable insights for the development of reliable and versatile biometric systems, with potential applications in security, forensics, and personalized identification

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Maiti, Diptadip, Madhuchhanda Basak, and Debashis Das. "Quality and Feature Analysis of Parent and Child Fingerprints in West Bengal, India." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (October 31, 2023): 127–41. http://dx.doi.org/10.22214/ijraset.2023.55946.

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Abstract: Fingerprint image of father, mother and child in ink paper method are collected from the region of West Bengal, India. One Hundred family without having kinship relation are involved in the data collection process. The paper is scanned with a HP scanner with 1200 ppi resolution and pre-processed with different technique to form a dataset of 1500 fingerprint images. Different non reference and reference image quality assessment technique are applied to test the quality of the image dataset. Minutiae based feature are extracted from the images to find a correlation between child and parent’s fingerprints. This family fingerprint dataset can be used by the researchers to find an alternative of DNA matching for parental verification or other research works.

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Tilak B and Vaishnavi N. M.Sc., M.Phil. (Ph.D.). "Online Fingerprint Authentication Scheme over Outsourced Data." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (March 15, 2025): 1288–93. https://doi.org/10.32628/cseit25112444.

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With the rifeness of mobile plans and the growth of biometric technology, biometric credentials, which can realize separate verification trusts on individual life or social physiognomies, has involved widely substantial interest. Though, confidentiality subjects of biometric data bring out increasing worries due to the extremely compassion of biometric data. Aiming at this test, in this project, we current a novel privacy-preserving online fingerprint verification arrangement, named e-Finga, over encoded subcontracted data. In the proposed e-Finga scheme, the user’s fingerprint registered in trust authority can be subcontracted to dissimilar servers with user’s approval, and safe, precise and well-organized verification service can be provided without the leakage of fingerprint information. Exactly, an better homomorphic encryption skill for secure Euclidean distance calculation to realize an efficient online fingerprint matching algorithm over encrypted Finger Code data in the subcontracting scenarios. Through detailed safety analysis, we show that e-Finga can fight various security intimidations. In addition, we implement e-Finga over a workstation with a real fingerprint database, and extensive imitation results prove that the proposed e-Finga scheme can serve well-organized and precise online fingerprint verification.

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Takeuchi, Hideyo, Taizo Umezaki, Noriyuki Matsumoto, and Katsumi Hirabayashi. "Speed-up of Fingerprint Verification Using Similarity of Fingerprint Spectrums." IEEJ Transactions on Electronics, Information and Systems 124, no. 3 (2004): 812–13. http://dx.doi.org/10.1541/ieejeiss.124.812.

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Parenreng, Jumadi Mabe, Nur Fadiah, Irmawati Irmawati, Syahrul Syahrul, Abdul Wahid, and M. Syahid Nur Wahid. "Solution for Public Smart Dispenser Using Digital Payment Based on the Fingerprint Minutiae Algorithm." JOIV : International Journal on Informatics Visualization 8, no. 3-2 (November 28, 2024): 1810. https://doi.org/10.62527/joiv.8.3-2.3309.

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Abstract (sommario):

Technological advancements have significantly focused on secure and efficient digital payments. In the context of Public Smart Dispensers (PSDs), using authentication and verification in payment transactions is crucial to address security concerns, enhance transaction efficiency, and provide a better user experience. This study employs minutiae algorithms for the fingerprint identification and verification process. Fingerprint identification utilizes the crossing number method, while fingerprint verification uses a validation score. If the validation score exceeds the threshold of >80, fingerprint verification is considered successful; conversely, verification is deemed unsuccessful if the validation score is <80. Through testing, biometrics as a payment method was conducted 100 times, resulting in an accuracy rate of 94% with an identification response time of approximately two or three seconds. The research findings demonstrate the practicality of implementing fingerprint biometric payment methods with minutiae algorithms on Public Smart Dispenser payment systems in the field of digital payments and technology. This enables fast and efficient transactions, significantly reducing the risk of fund misuse. Consequently, users can easily access water through Public Smart Dispensers, underscoring the real-world applicability and relevance of this solution. Implementing this technology can enhance user comfort and security while expediting the transaction process, which is crucial for public use. Therefore, this research makes a significant contribution to the advancement of fingerprint-based payment technology on public smart dispensers.

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AL-Jumaili, Ahmed Sabah Ahmed, Huda Kadhim Tayyeh, and Abeer Alsadoon. "AlexNet Convolutional Neural Network Architecture with Cosine and Hamming Similarity/Distance Measures for Fingerprint Biometric Matching." Baghdad Science Journal 20, no. 6(Suppl.) (December 5, 2023): 2559. http://dx.doi.org/10.21123/bsj.2023.8362.

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Abstract (sommario):

In information security, fingerprint verification is one of the most common recent approaches for verifying human identity through a distinctive pattern. The verification process works by comparing a pair of fingerprint templates and identifying the similarity/matching among them. Several research studies have utilized different techniques for the matching process such as fuzzy vault and image filtering approaches. Yet, these approaches are still suffering from the imprecise articulation of the biometrics’ interesting patterns. The emergence of deep learning architectures such as the Convolutional Neural Network (CNN) has been extensively used for image processing and object detection tasks and showed an outstanding performance compared to traditional image filtering techniques. This paper aimed to utilize a specific CNN architecture known as AlexNet for the fingerprint-matching task. Using such an architecture, this study has extracted the significant features of the fingerprint image, generated a key based on such a biometric feature of the image, and stored it in a reference database. Then, using Cosine similarity and Hamming Distance measures, the testing fingerprints have been matched with a reference. Using the FVC2002 database, the proposed method showed a False Acceptance Rate (FAR) of 2.09% and a False Rejection Rate (FRR) of 2.81%. Comparing these results against other studies that utilized traditional approaches such as the Fuzzy Vault has demonstrated the efficacy of CNN in terms of fingerprint matching. It is also emphasizing the usefulness of using Cosine similarity and Hamming Distance in terms of matching.

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