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Tytuł artykułu

Wybrane metody rozpoznawania osób na podstawie odcisków palców

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Treść / Zawartość
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Warianty tytułu
EN
Fingerprint recognition : review of used methods
Języki publikacji
PL
Abstrakty
PL
W artykule przedstawiono zagadnienie rozpoznawania tożsamości osób na podstawie odcisków palców. Przedstawiono aktualny stan wiedzy, wybrane metody i techniki zarówno opisu obrazu linii papilarnych, jak i metody klasyfikacji.
EN
The paper considers the issue of the identity recognition of persons on the basis of fingerprints. The current state of knowledge, selected methods and techniques of fingerprint image description and classification methods are presented.
Twórcy
autor
  • Instytut Teleinformatyki i Cyberbezpieczeństwa, Wydział Cybernetyki, WAT, ul. gen. Sylwestra Kaliskiego 2, 00-908 Warszawa
Bibliografia
  • [1] Ahmed F., Moskowitz I.S., Composite signature based watermarking for fingerprint authentication, MM&Sec ’05: Proceedings of the 7th workshop on Multimedia & Security, ACM, August 2005, pp. 137-142.
  • [2] Andrew T.B.J., David N.C.L., Integrated Wavelet and Fourier-Mellin invariant feature in fingerprint verification system. WBMA ’03: Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications, ACM, November 2003, pp. 82-88.
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  • [4] Bian W., Xu D., Li O., Cheng Y., Jie B., Ding X., A Survey of the Methods on Fingerprint Orientation Field Estimation. IEEE Access, 2019, Volume 7, pp. 32644-32663.
  • [5] Bolle R.M., Connell J.H., Pankanti S., Ratha N.K., Senior A.W., Biometria, WNT, Warszawa 2008.
  • [6] Cappelli M., Maio D., Maltoni D., Fingerprint Classification based on Multi-space KL. In proceedings Workshop on Automatic Identification Advances Technologies (AutoID ’99), Summit (NJ), October 1999, pp. 117-120.
  • [7] Cappelli M., Maio D., Maltoni D., Multi-space KL for Pattern Representation and Classification. IEEE Transactions on Pattern Analysis Machine Intelligence, Vol. 23, no. 9, September 2001, pp. 977-996.
  • [8] Cappelli R., Lumini A., Maio D., Maltoni D., Fingerprint Classification by Directional Image Partitioning. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(5), 1999, pp. 402-421.
  • [9] Cappelli M., Maio D., Maltoni D, Nanni L., A two-stage fingerprint classification system. WBMA ’03: Proceedings of the 2003 ACM SIGMM workshop on biometrics methods and applications, ACM, November 2003, pp. 95-99.
  • [10] Cao K., Jain A.K., Automated Latent Fingerprint Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, Volume 41, Issue 4, pp. 788-800.
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  • [13] Gour B., Bandopadhyaya T.K., Patel R., ART and Modular Neural Network Architecture for Multilevel Categorization and Recognition of Fingerprints. IEEE Conference, Knowledge Discovery and Data Mining, 2010. WKDD ’10, Third International Conference, pp. 536-539.
  • [14] Gupta P., Ravi S., Raghunathan A., Jha N.K., Efficient fingerprint-based user authentication for embedded systems. DAC ’05: Proceedings of the 42nd annual Design Automation Conference, ACM, June 2005.
  • [15] Holz Ch., Baudisch P., The generalized perceived input point model and how to double touch accuracy by extracting fingerprints. CHI ’10: Proceedings of the 28th international conference on Human factors in computing systems, ACM, April 2010.
  • [16] Hong L., Jain A.K., Classification of Fingerprint Images. http://www.cse.msu.edu/biometrics/Publications/Fingerprint/clas.pdf
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  • [18] Jain A.K., Hong L., Pankanti S., Bolle R., An Identity Autentication System Using Fingerprints. Proc. of IEEE 85 (9), 1997, pp. 1365-1388, on line at: http://biometrics.cse.msu.edu/Publications/Fingerprint/JainEtAlIdentityAuthUsingFp_ProcIEEE97.pdf
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  • [21] Kapczyński A., Quantitative and qualitative characteristics of fingerprint biometric templates. Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska, 2014, z. 74, s. 55-63.
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  • [23] Kwiatkowski W., Metody rozpoznawania wzorców. Bel Studio, Warszawa, 2002.
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  • [25] Mil’shtein S., Pillai A., Shendye A., Liessner C., Baier M., Fingerprint Recognition Algorithms for Partial and Full Fingerprints. 2008 IEEE Conference on Technologies for Homeland Security, pp. 449-452.
  • [26] Montesanto A., Baldassarri P., Vallesi G., Tascini G., Fingerprints recognition using Minutae extraction: a fuzzy approach, Image analysis and processing, ICIAP 2007, 14th International Conference, pp. 229-234.
  • [27] Park C.H., Lee J.J., Smith M., Park S., Park K.H., Directional filter bank-based fingerprint feature extraction and matching. IEEE Trans. On Circuits and Systems for Video Tachnology, Vol. 14, 1, 2004, pp. 74-78.
  • [28] Rao A.R., A Taxonomy for Texture Description and Identification. Springer-Verlag, New York, 1990.
  • [29] Rapta P., Saeed K., A new algorithm for fingerprint feature extraction without the necessity to improve its image. Bio-Algorithms and Med-Systems, 2010, Vol. 6, no. 12, pp. 25-29.
  • [30] Srinivasan V.S., Murthy N.N., Detection of Singular Points In Fingerprint Images. Pattern Recognition 25(2), 1992, pp. 139-153.
  • [31] Surmacz K., Saeed K., Rapta P., An improved algorithm for feature extraction from a fingerprint fuzzy image. Optica Applicata, 2013, Vol. 43, no. 3, pp. 515-527.
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  • [33] Ślot K., Wybrane zagadnienia biometrii. WKŁ, Warszawa, 2008.
  • [34] Tang T.Y., Moon Y.S., Chan K.C., Efficient implementation of fingerprint verification for mobile embedded systems using fixed-point arithmetic. SAC ’04: Proceedings of the 2004 ACM symposium on Applied Computing, ACM, March 2004, pp. 821-825.
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  • [36] Tico M., Immonen E., Ramo P., Kousmanen P., Sarinnen J., Fingerprint Recognition Using Wavelet Features. Proc. of IEEE international Symposium on Circuits and Systems 2, 2001, pp. 21-24.
  • [37] Yang S., Verbauwhede I.M., A secure fingerprint matching technique. WBMA ’03: Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications, ACM, November, 2003, pp. 98-94.
  • [38] Wang S., Zhang W.W., Wang Y.S., Fingerprints Classification by Directional Fields. ICMI'02: Proceedings of the 4th IEEE International Conference on Multimodal Interfaces, IEEE Computer Society, October, 2002, pp. 395-399.
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  • [40] Wieclaw L., Gradient based fingerprint orientation field estimation. Journal of Medical Informatics & Technologies, Vol. 22, 2013, pp. 203-207.
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  • [42] Wójtowicz W., A Fingerprint-Based Digital Images Watermarking for Identity Authentication. Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica, Vol. 14, no. 1, 2014, pp. 85-96.
  • [43] Valdes-Ramirez D., Medina-Pérez M.A., Monroy R., Loyola-González O., Rodríguez J., Morales A., Herrera F., A Review of Fingerprint Feature Representations and Their Applications for Latent Fingerprint Identification: Trends and Evaluation. IEEE Access, Volume 7, 2019, pp. 48484-48499.
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  • [45] Zhang Q., Huang K., Yan H., Fingerprint classification based on extraction and analysis of singularities and pseudo ridges. VIP '01: Proceedings of the Pan-Sydney area workshop on Visual information processing, Volume 11, Australian Computer Society Inc., May, 2001.
  • [46] Zhao F., Tang X., Preprocessing and postprocessing for skeleton-based fingerprint minutiae extraction. Pattern Recognition 40, 2007, pp. 1270-1281, online at: www.sciencedirect.com
  • [47] Żurada J., Barski M., Jędruch W., Sztuczne sieci neuronowe. Wydawnictwo Naukowe PWN, Warszawa, 1996.
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Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-4b40d1d9-9e25-4f39-b28a-a50f6fb32a23
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