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Articoli di riviste sul tema "Fingerprint Verification"

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Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 19 febbraio 2022

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1

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

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Abstract (sommario):

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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2

QIN, WEI, e YILONG YIN. "A NOVEL METHOD USING VIDEOS FOR FINGERPRINT VERIFICATION". International Journal of Pattern Recognition and Artificial Intelligence 26, n. 01 (febbraio 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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3

Upendra, K., S. Singh, V. Kumar e H. K. Verma. "Online fingerprint verification". Journal of Medical Engineering & Technology 31, n. 1 (gennaio 2007): 36–45. http://dx.doi.org/10.1080/03091900500324194.

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4

Djara, Tahirou, Marc Kokou Assogba e Antoine Vianou. "A Contactless Fingerprint Verification Method using a Minutiae Matching Technique". International Journal of Computer Vision and Image Processing 6, n. 1 (gennaio 2016): 12–27. http://dx.doi.org/10.4018/ijcvip.2016010102.

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Abstract (sommario):

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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5

Khan, Sajid, Dong-Ho Lee, Asif Khan, Ahmad Waqas, Abdul Rehman Gilal e Zahid Hussain Khand. "A Digital Camera-Based Rotation-Invariant Fingerprint Verification Method". Scientific Programming 2020 (15 maggio 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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6

Cappelli, Raffaele, Matteo Ferrara, Annalisa Franco e Davide Maltoni. "Fingerprint verification competition 2006". Biometric Technology Today 15, n. 7-8 (luglio 2007): 7–9. http://dx.doi.org/10.1016/s0969-4765(07)70140-6.

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7

Mesec, Chip. "Fingerprint identification versus verification". Biometric Technology Today 15, n. 9 (settembre 2007): 7. http://dx.doi.org/10.1016/s0969-4765(07)70157-1.

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8

Qin, Wei, Yilong Yin e Lili Liu. "Video-Based Fingerprint Verification". Sensors 13, n. 9 (4 settembre 2013): 11660–86. http://dx.doi.org/10.3390/s130911660.

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9

Maio, D., D. Maltoni, R. Cappelli, J. L. Wayman e A. K. Jain. "FVC2000: fingerprint verification competition". IEEE Transactions on Pattern Analysis and Machine Intelligence 24, n. 3 (marzo 2002): 402–12. http://dx.doi.org/10.1109/34.990140.

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10

Jain, A., Lin Hong e R. Bolle. "On-line fingerprint verification". IEEE Transactions on Pattern Analysis and Machine Intelligence 19, n. 4 (aprile 1997): 302–14. http://dx.doi.org/10.1109/34.587996.

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11

Kumar, Ravinder, Pravin Chandra e M. Hanmandlu. "Fingerprint Matching Based on Orientation Feature". Advanced Materials Research 403-408 (novembre 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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12

Wang, Feng. "Fusion Fingerprint and Face Multi-Biometrics Recognition Based on D-S Evidence Theory". Advanced Materials Research 459 (gennaio 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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13

Chan, K. C., Y. S. Moon e P. S. Cheng. "Fast Fingerprint Verification Using Subregions of Fingerprint Images". IEEE Transactions on Circuits and Systems for Video Technology 14, n. 1 (gennaio 2004): 95–101. http://dx.doi.org/10.1109/tcsvt.2003.818358.

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14

Praseetha, V. M., Saad Bayezeed e S. Vadivel. "Secure Fingerprint Authentication Using Deep Learning and Minutiae Verification". Journal of Intelligent Systems 29, n. 1 (10 aprile 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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15

Kirvel, Vitali. "Fingerprint analysis (AFIS) and biometric system of identification by fingerprints: issue of artificial papillary pictures". Общество и инновации 1, n. 3 (1 gennaio 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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16

Jiang, Yujia, e 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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17

Ramírez Flores, Manuel, Gualberto Aguilar Torres, Gina Gallegos García e Miguel Ángel García Licona. "Fingerprint verification using computational geometry". DYNA 83, n. 195 (23 febbraio 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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18

KIM, Bongil, Jaeyeon LEE, Chungkyue KIM, Kiyonari FUKUE, Haruhisa SHIMODA e Toshifumi SAKATA. "Development of fingerprint verification system." Journal of the Japan society of photogrammetry and remote sensing 28, n. 1 (1989): 4–18. http://dx.doi.org/10.4287/jsprs.28.4.

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19

Gamble, F. T., L. M. Frye e D. R. Grieser. "Real-time fingerprint verification system". Applied Optics 31, n. 5 (10 febbraio 1992): 652. http://dx.doi.org/10.1364/ao.31.000652.

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20

Khalil, Mohammed S., Dzulkifli Mohamad, Muhammad Khurram Khan e Qais Al-Nuzaili. "Fingerprint verification using statistical descriptors". Digital Signal Processing 20, n. 4 (luglio 2010): 1264–73. http://dx.doi.org/10.1016/j.dsp.2009.12.002.

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21

GONZÁLEZ, MARIO, DAVID DOMINGUEZ, FRANCISCO B. RODRÍGUEZ e ÁNGEL SÁNCHEZ. "RETRIEVAL OF NOISY FINGERPRINT PATTERNS USING METRIC ATTRACTOR NETWORKS". International Journal of Neural Systems 24, n. 07 (10 ottobre 2014): 1450025. http://dx.doi.org/10.1142/s0129065714500257.

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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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22

Lalovic, Komlen, Milan Milosavljevic, Ivan Tot e Nemanja Macek. "Device for biometric verification of maternity". Serbian Journal of Electrical Engineering 12, n. 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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23

Le, Ngoc Tuyen, Duc Huy Le, Jing-Wein Wang e Chih-Chiang Wang. "Entropy-Based Clustering Algorithm for Fingerprint Singular Point Detection". Entropy 21, n. 8 (12 agosto 2019): 786. http://dx.doi.org/10.3390/e21080786.

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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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24

Jo, Young-Hoo, Seong-Yun Jeon, Jong-Hyuk Im e 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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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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25

Takeuchi, Hideyo, Taizo Umezaki, Noriyuki Matsumoto e Katsumi Hirabayashi. "Speed-up of Fingerprint Verification Using Similarity of Fingerprint Spectrums". IEEJ Transactions on Electronics, Information and Systems 124, n. 3 (2004): 812–13. http://dx.doi.org/10.1541/ieejeiss.124.812.

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26

Yakubu, Osman, e Osei Adjei. "Reliability of Fingerprint Verification in Ghana". International Journal of Computer Applications 107, n. 10 (18 dicembre 2014): 9–13. http://dx.doi.org/10.5120/18785-0116.

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27

Medina-Pérez, Miguel Angel, Milton García-Borroto, Andres Eduardo Gutierrez-Rodríguez e Leopoldo Altamirano-Robles. "Improving Fingerprint Verification Using Minutiae Triplets". Sensors 12, n. 3 (8 marzo 2012): 3418–37. http://dx.doi.org/10.3390/s120303418.

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28

Saeed, MustafaGhanem, FahadLayth Malallah e FouadSaleem Tawfee. "CHILDREN TRACKING SYSTEM USING FINGERPRINT VERIFICATION." International Journal of Advanced Research 4, n. 12 (31 dicembre 2016): 1381–91. http://dx.doi.org/10.21474/ijar01/2534.

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29

B.Baskar, D. Sriganesh,. "Combined Fingerprint Verification for Privacy Protection". International Journal of Innovative Research in Computer and Communication Engineering 03, n. 02 (28 febbraio 2015): 1043–49. http://dx.doi.org/10.15680/ijircce.2015.0302047.

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30

Alzahrani, Hamdan Ahmed. "Remote Authentication Using Vaulted Fingerprint Verification". ELCVIA Electronic Letters on Computer Vision and Image Analysis 15, n. 2 (8 novembre 2016): 37. http://dx.doi.org/10.5565/rev/elcvia.985.

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31

Prabhakar, Salil, e Anil K. Jain. "Decision-level fusion in fingerprint verification". Pattern Recognition 35, n. 4 (aprile 2002): 861–74. http://dx.doi.org/10.1016/s0031-3203(01)00103-0.

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32

O'Gorman, Lawrence. "An overview of fingerprint verification technologies". Information Security Technical Report 3, n. 1 (gennaio 1998): 21–32. http://dx.doi.org/10.1016/s1363-4127(98)80015-0.

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O'Gorman, Lawrence. "An overview of fingerprint verification technologies". Information Security Technical Report 4 (gennaio 1999): 28–29. http://dx.doi.org/10.1016/s1363-4127(99)80054-5.

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34

Hollingum, Jack. "AUTOMATED FINGERPRINT ANALYSIS OFFERS FAST VERIFICATION". Sensor Review 12, n. 3 (marzo 1992): 12–15. http://dx.doi.org/10.1108/eb007878.

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35

Haiyun Xu, R. N. J. Veldhuis, A. M. Bazen, T. A. M. Kevenaar, T. A. H. M. Akkermans e B. Gokberk. "Fingerprint Verification Using Spectral Minutiae Representations". IEEE Transactions on Information Forensics and Security 4, n. 3 (settembre 2009): 397–409. http://dx.doi.org/10.1109/tifs.2009.2021692.

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36

Cappelli, R., D. Maio, D. Maltoni, J. L. Wayman e A. K. Jain. "Performance evaluation of fingerprint verification systems". IEEE Transactions on Pattern Analysis and Machine Intelligence 28, n. 1 (gennaio 2006): 3–18. http://dx.doi.org/10.1109/tpami.2006.20.

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Yager, Neil, e Adnan Amin. "Dynamic registration selection for fingerprint verification". Pattern Recognition 39, n. 11 (novembre 2006): 2141–48. http://dx.doi.org/10.1016/j.patcog.2006.02.020.

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Hattori, Koosuke, Ryo Taguchi, Taizo Umezaki, Yuhei Harada, Masahiro Hoguro e Tatsuki Yoshimine. "Fingerprint Verification Algorithm with Polar-sampling". IEEJ Transactions on Electronics, Information and Systems 136, n. 7 (2016): 938–44. http://dx.doi.org/10.1541/ieejeiss.136.938.

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Han, Huiyi, Zhiyi Qu e Weiwei Kang. "A LOGIN STRATEGY USING FINGERPRINT VERIFICATION". International Journal of Computer Science and Engineering 2, n. 7 (25 luglio 2015): 1–5. http://dx.doi.org/10.14445/23488387/ijcse-v2i7p111.

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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 (24 settembre 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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41

Hassan, Norsalina, Dzati Athiar Ramli e Shahrel Azmin Suandi. "Fusion of Face and Fingerprint for Robust Personal Verification System". International Journal of Machine Learning and Computing 4, n. 4 (2014): 371–75. http://dx.doi.org/10.7763/ijmlc.2014.v4.439.

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42

KONVALINKA, IRA. "VERIFICATION SPEED IN FINGERPRINT-BASED BIOMETRIC SYSTEMS". International Journal of Image and Graphics 03, n. 03 (luglio 2003): 447–59. http://dx.doi.org/10.1142/s021946780300110x.

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Issues with verification speed improvement to fingerprint systems are investigated in this paper. First, the impact of verification speed on the overall system performance is highlighted. Then a rather general speed enhancement procedure for algorithms is proposed, consisting of two or more pattern matching stages for refining the value of the discrimination function. Special attention is paid to the algorithm supporting the verification procedure used to determine the two verification thresholds. It is shown, and supported by numerical examples, that significant verification speed improvement can be achieved without sacrificing the system accuracy.

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43

Alimardani, Fatemeh, e Reza Boostani. "IMPROVEMENT OF THE PERFORMANCE OF FINGERPRINT VERIFICATION USING A COMBINATORIAL APPROACH". Biomedical Engineering: Applications, Basis and Communications 30, n. 03 (30 maggio 2018): 1850019. http://dx.doi.org/10.4015/s1016237218500199.

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Fingerprint verification systems have attracted much attention in secure organizations; however, conventional methods still suffer from unconvincing recognition rate for noisy fingerprint images. To design a robust verification system, in this paper, wavelet and contourlet transforms (CTS) were suggested as efficient feature extraction techniques to elicit a coverall set of descriptive features to characterize fingerprint images. Contourlet coefficients capture the smooth contours of fingerprints while wavelet coefficients reveal its rough details. Due to the high dimensionality of the elicited features, across group variance (AGV), greedy overall relevancy (GOR) and Davis–Bouldin fast feature reduction (DB-FFR) methods were adopted to remove the redundant features. These features were applied to three different classifiers including Boosting Direct Linear Discriminant Analysis (BDLDA), Support Vector Machine (SVM) and Modified Nearest Neighbor (MNN). The proposed method along with state-of-the-art methods were evaluated, over the FVC2004 dataset, in terms of genuine acceptance rate (GAR), false acceptance rate (FAR) and equal error rate (EER). The features selected by AGV were the most significant ones and provided 95.12% GAR. Applying the selected features, by the GOR method, to the modified nearest neighbor, resulted in average EER of [Formula: see text]%, which outperformed the compared methods. The comparative results imply the statistical superiority ([Formula: see text]) of the proposed approach compared to the counterparts.

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44

Nwankwo, C., G. Oletu e O. B. Longe. "Security Enhancement of an Automated Teller Machine Using Fingerprint and Password". advances in multidisciplinary & scientific research journal publication 26, n. 1 (11 dicembre 2020): 45–50. http://dx.doi.org/10.22624/isteams/v26p5-ieee-ng-ts.

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This paper focuses on how to enhanced security of Transactions in Automatic Teller Machine system using a multi-factor authentication system (Password and Fingerprint). In order to achieve security and to overcome illegal activities, shortcoming of piracy in money transactions, we propose the idea of using fingerprints of customers and password instead of traditional use of PIN number. After authorized verification, the customer will be able to proceed for transaction else after four successive wrong attempts, the ATM card will be ejected automatically and a message will be sent to the registered mobile number. The proposed system is developed to provide better security to the ATMs. Keywords— ATM, Fingerprint, PIN, Biometric.

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HU, Zhiqiang, Dongju LI, Tsuyoshi ISSHIKI e Hiroaki KUNIEDA. "Hybrid Minutiae Descriptor for Narrow Fingerprint Verification". IEICE Transactions on Information and Systems E100.D, n. 3 (2017): 546–55. http://dx.doi.org/10.1587/transinf.2016edp7256.

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Taileb, Mounira. "RFID and Fingerprint - Based Student Verification System". IJARCCE 8, n. 8 (30 agosto 2019): 34–43. http://dx.doi.org/10.17148/ijarcce.2019.8807.

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Arunalatha, J. S., V. Tejaswi, K. Shaila, Dinesh Anvekar, K. R. Venugopal, S. S. Iyengar e L. M. Patnaik. "FIVDL: Fingerprint Image Verification using Dictionary Learning". Procedia Computer Science 54 (2015): 482–90. http://dx.doi.org/10.1016/j.procs.2015.06.055.

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48

Zhang, Ping, Xi Guo e Jyotirmay Gadedadikar. "Online Fingerprint Verification Algorithm and Distributed System". Journal of Signal and Information Processing 02, n. 02 (2011): 79–87. http://dx.doi.org/10.4236/jsip.2011.22011.

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Gupta, Puneet, e Phalguni Gupta. "An accurate slap fingerprint based verification system". Neurocomputing 188 (maggio 2016): 178–89. http://dx.doi.org/10.1016/j.neucom.2015.01.111.

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Pankanti, Sharath. "Verification watermarks on fingerprint recognition and retrieval". Journal of Electronic Imaging 9, n. 4 (1 ottobre 2000): 468. http://dx.doi.org/10.1117/1.1287795.

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