Validating data acquired with experimental multimodal biometric system installed in bank branches - Publikacja - MOST Wiedzy


Validating data acquired with experimental multimodal biometric system installed in bank branches


An experimental system was engineered and implemented in 100 copies inside a real banking environment comprising: dynamic handwritten signature verification, face recognition, bank client voice recognition and hand vein distribution verification. The main purpose of the presented research was to analyze questionnaire responses reflecting user opinions on: comfort, ergonomics, intuitiveness and other aspects of the biometric enrollment process. The analytical studies and experimental work conducted in the course of this work will lead towards methodologies and solutions of the multimodal biometric technology, which is planned for further development. Before this stage is achieved a study on the data usefulness acquired from a variety of biometric sensors and from survey questionnaires filled in by banking tellers and clients was done. The decision-related sets were approximated by the Rough Set method offering efficient algorithms and tools for finding hidden patterns in data. Prediction of evaluated biometric data quality, based on enrollment samples and on user subjective opinions was made employing the developed method. After an introduction to the principles of applied biometric identity verification methods, the knowledge modelling approach is presented together with achieved results and conclusions.


  • 7


  • 0

    Web of Science

  • 1 2


Cytuj jako

Pełna treść

pobierz publikację
pobrano 42 razy
Wersja publikacji
Accepted albo Published Version
Creative Commons: CC-BY otwiera się w nowej karcie

Słowa kluczowe

Informacje szczegółowe

Publikacja w czasopiśmie
artykuł w czasopiśmie wyróżnionym w JCR
Opublikowano w:
ISSN: 0925-9902
Rok wydania:
Opis bibliograficzny:
Szczuko P., Czyżewski A., Hoffmann P., Bratoszewski P., Lech M.: Validating data acquired with experimental multimodal biometric system installed in bank branches// JOURNAL OF INTELLIGENT INFORMATION SYSTEMS. -Vol. 52, iss. 1 (2019), s.1-32
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1007/s10844-017-0491-2
Bibliografia: test
  1. Alize (2017). Open source recognition, University of Avignon, Accessed: 01 Oct 2017. otwiera się w nowej karcie
  2. Banerjee, M., Mitra, S., Banka, H. (2007). Evolutionary rough feature selection in gene expression data. IEEE Transactions on Systems, Man, and Cybernetics Part C: Applications and Reviews, 37(4), 622-632. otwiera się w nowej karcie
  3. Bazan, J.G., Nguyen, H.S., Nguyen, S.H., Synak, P., Wroblewski, J. (2000). Rough set algorithms in clas- sification problem, chapter 2. In Polkowski, L., Tsumoto, S., Lin, T.Y. (Eds.) 49-88. Heidelberg: Physica-Verlag, otwiera się w nowej karcie
  4. Bazan, J.G., Peters, J.F., Skowron, A. (2005). Behavioral pattern identification through rough set modelling. InŚlėzak, D., Yao, J., Peters, J.F., Ziarko, W., Hu, X. (Eds.) Rough sets, fuzzy sets, data mining, and granular computing. RSFDGrC 2005. Lecture notes in computer science, Vol. 3642. Berlin: Springer. otwiera się w nowej karcie
  5. Bhele, S.G., & Mankar, V.H. (2015). Recognition of faces using discriminative features of LBP and HOG descriptor in varying environment. In 2015 International conference on computational intelligence and communication networks (CICN) (pp. 426-432). Jabalpur. otwiera się w nowej karcie
  6. Borade, S.N., Deshmukh, R.R., Ramu, S. (2016). Face recognition using fusion of PCA and LDA: Borda count approach. In 2016 24th Mediterranean conference on control and automation (MED) (pp. 426- 432). Athens. otwiera się w nowej karcie
  7. Braga, M. (2017). Facial recognition technology is coming to Canadian airports this spring, CBC News, 4007344. Accessed 01 Oct 2017. otwiera się w nowej karcie
  8. Bratoszewski, P., & Czyżewski, A. (2015). Face profile view retrieval using time of flight camera image analysis. In Kryszkiewicz, M., Bandyopadhyay, S., Rybinski, H., Pal, S. (Eds.) Pattern recognition and machine intelligence. PReMI 2015. Lecture notes in computer science, Vol. 9124: Springer, otwiera się w nowej karcie
  9. Bratoszewski, P., Czyżewski, A., Hoffmann, P., Lech, M., Szczodrak, M. (2017). Pilot testing of developed multimodal biometric identity verification system. In Proc. signal processing, algorithms, architectures, arrangements, and applications (pp. 184 -189). Poznań, 20.9.2017-22.9.2017. otwiera się w nowej karcie
  10. Chen, W., Hong, Q., Li, X. (2012). GMM-UBM for text-dependent speaker recognition. In International conference on audio, language and image processing (pp. 432-435). Shanghai. otwiera się w nowej karcie
  11. Fujitsu Identity Management and PalmSecure (2017). Global_Solution_Catalogue.pdf. Accessed 01 Oct 2017. otwiera się w nowej karcie
  12. Furui, S. (1982). Comparison of speaker recognition methods using statistical features and dynamic features. IEEE Transactions on Acoustics, Speech, and Signal Processing, 29, 342-350. otwiera się w nowej karcie
  13. Gardener, M., & Beginning, R. (2016). The statistical programming language. See also: https://cran.r-project. org/manuals.html. Accessed 01 Oct 2016.
  14. Gauvain, L., & Lee, C.-H. (1994). Maximum a posteriori estimation for multivariate gaussian mixture obser- vations of Markov chains. In IEEE International conference on acoustics, speech, and signal processing, ICASSP (Vol 2, pp. 291-298). otwiera się w nowej karcie
  15. Gupta, A., & Gupta, H. (2013). Applications of MFCC and vector quantization in speaker recognition. In 2013 International conference on intelligent systems and signal processing (ISSP) (pp. 170-173). Gujarat. otwiera się w nowej karcie
  16. Janusz, A., & Stawicki, S. (2012). Applications of approximate reducts to the feature selection problem. Proceedings of International Conference on Rough Sets and Knowledge Technology (RSKT), 6954, 45- 50. otwiera się w nowej karcie
  17. Jiang, H. (2005). Confidence measures for speech recognition a survey. Speech Communication, 45(4), 455- 470. otwiera się w nowej karcie
  18. Klontz, J.C., Klare, B.F., Klum, S., Jain, A.K., Burge, M.J. (2013). Open source biometric recognition. In 2013 IEEE Sixth international conference on biometrics: theory, applications and systems (BTAS) (pp. 1-8). IEEE. otwiera się w nowej karcie
  19. Larcher, A., Bonastre, J.-F., Fauve, B.G.B., Lee, K.-A., Levy, H., Li, H., Mason, J.D.D., Parfait, J.-Y. (2013).
  20. ALIZE 3.0 -open source toolkit for state-of-the-art speaker recognition. In Proceedings of the annual conference of the international speech communication association, INTERSPEECH (pp. 2768-2772). otwiera się w nowej karcie
  21. Lech, M., & Czyżewski, A. (2016). A handwritten signature verification method employing a tablet. Signal Processing, Algorithms, Architectures, Arrangements, and Applications, Poznań, 21.9.2016-23.9.2016. otwiera się w nowej karcie
  22. Lech, M., Bratoszewski, P., Czyżewski, A. (2016). A handwriten signature verification system XXXII Krajowe Sympozjum Telekomunikacji i Teleinformatyki, Gliwice Przeglȧd Telekomunikacyjny + Wiadomości Telekomunikacyjne. otwiera się w nowej karcie
  23. Jin, J., & Zhang, L. (2014). Celebrity face image retrieval using multiple features. In International conference on neural information processing (pp. 119-126). Cham: Springer. otwiera się w nowej karcie
  24. Mazumdar, D., Mitra, S., Mitra, S. (2010). Evolutionary-rough feature selection for face recognition. In Peters, J.F., Skowron, A., Słowiński, R., Lingras, P., Miao, D., Tsumoto, S. (Eds.) Transactions on rough sets XII. Lecture Notes In Computer Science, Vol. 6190. Berlin: Springer. otwiera się w nowej karcie
  25. McCool, C., Marcel, S., Hadid, A., Pietikinen, M., Matjka, P. (2012). Bi-modal person recognition on a mobile phone: using mobile phone data. In IEEE ICME Workshop on hot topics in mobile mutlimedia. Melbourne. otwiera się w nowej karcie
  26. Mermelstein, D. (1980). Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Transactions on Acoustics, Speech, and Signal Processing, 28(4), 357-366.
  27. Nguyen, S.H. (2001). On efficient handling of continuous attributes in large data bases. Fundamenta Informatics, 48(1), 61-81.
  28. Papatheodorou, T., & Rueckert, D. (2007). 3D face recognition, face recognition. In Kresimir D., and Mislav G., (eds.), InTech. otwiera się w nowej karcie
  29. Pawlak, Z. (1982). Rough sets. International Journal of Computer and Information Sciences, 11, 341. otwiera się w nowej karcie
  30. Pawlak, Z. (1991). Rough sets theoretical aspects of reasoning about data. Kluwer. otwiera się w nowej karcie
  31. Riza, S.L., Janusz, A.,Ślęzak, D., Cornelis, C., Herrera, F., Benitez, J.M., Bergmeir, C., Stawicki, S. (2015). RoughSets: data analysis using rough set and fuzzy rough set theories. janusza/RoughSets. Accessed 01 Oct 2016, html, Accessed 01 Oct 2016. otwiera się w nowej karcie
  32. Shanker, P., & Rajagopalan, A. (2007). A.N.: off-line signature verification using DTW. Pattern Recognition Letters, 28, 1407-1414.
  33. Szczodrak, M., & Czyżewski, A. (2017). Evaluation of face detection algorithms for the bank client identity verification. Foundations of Computing and Decision Sciences, 42(2), 137-148. otwiera się w nowej karcie
  34. Tsumoto, S. (2002). Discovery of approximate knowledge in medical databases based on rough set model. In Lin, T.Y., Yao, Y.Y., Zadeh, L. (Eds.) Data mining, rough sets and granular computing. Studies in fuzziness and soft computing, Vol. 95. Heidelberg: Physica. otwiera się w nowej karcie
  35. Zhong, N., Dong, J., Ohsuga (2001). Using rough sets with heuristics for feature selection S. Journal of Intelligent Information Systems, 16, 199. otwiera się w nowej karcie
Politechnika Gdańska

wyświetlono 163 razy

Publikacje, które mogą cię zainteresować

Meta Tagi