Validating data acquired with experimental multimodal biometric system installed in bank branches - Publication - Bridge of Knowledge

Search

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

Abstract

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.

Citations

  • 7

    CrossRef

  • 0

    Web of Science

  • 1 2

    Scopus

Cite as

Full text

download paper
downloaded 42 times
Publication version
Accepted or Published Version
License
Creative Commons: CC-BY open in new tab

Keywords

Details

Category:
Articles
Type:
artykuł w czasopiśmie wyróżnionym w JCR
Published in:
JOURNAL OF INTELLIGENT INFORMATION SYSTEMS no. 52, pages 1 - 32,
ISSN: 0925-9902
Language:
English
Publication year:
2019
Bibliographic description:
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
DOI:
Digital Object Identifier (open in new tab) 10.1007/s10844-017-0491-2
Bibliography: test
  1. Alize (2017). Open source recognition, University of Avignon, http://mistral.univ-avignon.fr. Accessed: 01 Oct 2017. open in new tab
  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. open in new tab
  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, https://doi.org/10.1007/978-3-7908-1840-6_3. open in new tab
  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. open in new tab
  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. open in new tab
  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. https://doi.org/10.1142/S0219467806002239. open in new tab
  7. Braga, M. (2017). Facial recognition technology is coming to Canadian airports this spring, CBC News, http://www.cbc.ca/news/technology/cbsa-canada-airports-facial-recognition-kiosk-biometrics-1. 4007344. Accessed 01 Oct 2017. open in new tab
  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, https://doi.org/10.1007/978-3-319-19941-2_16. open in new tab
  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. open in new tab
  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. open in new tab
  11. Fujitsu Identity Management and PalmSecure (2017). https://www.fujitsu.com/au/Images/PalmSecure_ Global_Solution_Catalogue.pdf. Accessed 01 Oct 2017. open in new tab
  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. open in new tab
  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). open in new tab
  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. open in new tab
  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. open in new tab
  17. Jiang, H. (2005). Confidence measures for speech recognition a survey. Speech Communication, 45(4), 455- 470. https://doi.org/10.1016/j.specom.2004.12.004. open in new tab
  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. open in new tab
  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). open in new tab
  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. https://doi.org/10.1109/SPA.2016.7763585. open in new tab
  22. Lech, M., Bratoszewski, P., Czyżewski, A. (2016). A handwriten signature verification system XXXII Krajowe Sympozjum Telekomunikacji i Teleinformatyki, Gliwice Przeglȧd Telekomunikacyjny + Wiadomości Telekomunikacyjne. https://doi.org/10.15199/59.2016.8-9.77. open in new tab
  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. open in new tab
  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. open in new tab
  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. open in new tab
  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. https://doi.org/10.5772/4848. open in new tab
  29. Pawlak, Z. (1982). Rough sets. International Journal of Computer and Information Sciences, 11, 341. https://doi.org/10.1007/BF01001956. open in new tab
  30. Pawlak, Z. (1991). Rough sets theoretical aspects of reasoning about data. Kluwer. open in new tab
  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. https://github.com/ janusza/RoughSets. Accessed 01 Oct 2016, https://cran.r-project.org/web/packages/RoughSets/index. html, Accessed 01 Oct 2016. open in new tab
  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. https://doi.org/10.1515/fcds-2017-0006. open in new tab
  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. open in new tab
  35. Zhong, N., Dong, J., Ohsuga (2001). Using rough sets with heuristics for feature selection S. Journal of Intelligent Information Systems, 16, 199. https://doi.org/10.1023/A:1011219601502. open in new tab
Verified by:
Gdańsk University of Technology

seen 165 times

Recommended for you

Meta Tags