http://iet.metastore.ingenta.com
1887

access icon openaccess Analysis of results of large-scale multimodal biometric identity verification experiment

  • PDF
    1.1758222579956055MB
  • XML
    117.8671875Kb
  • HTML
    110.1552734375Kb
Loading full text...

Full text loading...

/deliver/fulltext/iet-bmt/8/1/IET-BMT.2018.5030.html;jsessionid=56ypg05mbhhy.x-iet-live-01?itemId=%2fcontent%2fjournals%2f10.1049%2fiet-bmt.2018.5030&mimeType=html&fmt=ahah

References

    1. 1)
      • 1. Ross, A., Jain, A.K.: ‘Multimodal biometrics: an overview’. 12th European Signal Processing Conf. (EUSIPCO), Vienna, Austria, 2004, pp. 12211224.
    2. 2)
      • 2. Jain, A., Nandakumar, K., Ross, A.: ‘Score normalization in multimodal biometric systems’, Pattern Recognit., 2005, 38, (12), pp. 22702285, doi: 10.1016/j.patcog.2005.01.012.
    3. 3)
      • 3. Faundez-Zanuy, M., Fierrez-Aguilar, J., Ortega-Garcia, J., et al: ‘Multimodal biometric databases: an overview’, IEEE Aerosp. Electron. Syst. Mag., 2006, 21, (8), pp. 2937, doi: 10.1109/MAES.2006.1703234.
    4. 4)
      • 4. Garcia-Salicetti, S., Beumier, C., Chollet, G., et al: ‘BIOMET: a multimodal person authentication database including face, voice, fingerprint, hand and signature modalities’. Audio- and Video-Based Biometric Person Authentication (AVBPA 2003), Berlin, (LNCS, 2688), 2003, doi: 10.1007/3-540-44887-X_98.
    5. 5)
      • 5. Ortega-Garcia, J., Fierrez-Aguilar, J., Simon, D., et al: ‘MCYT baseline corpus: a bimodal biometric database’, IEE Proc., Vis. Image Signal Process., 2003, 150, (6), pp. 395401, doi: 10.1049/ip-vis:20031078.
    6. 6)
      • 6. Fierrez, J., Ortega-Garcia, J., Toledano, D.T., et al: ‘Biosec baseline corpus: a multimodal biometric database’, Pattern Recognit., 2007, 40, (4), pp. 13891392, doi: 10.1016/j.patcog.2006.10.014.
    7. 7)
      • 7. Fierrez, J., Galbally, J., Ortega-Garcia, J., et al: ‘BiosecurID: a multimodal biometric database’, Pattern Anal. Appl., 2010, 13, p. 235, doi: org/10.1007/s10044-009-0151-4.
    8. 8)
      • 8. Ho, C.C., Ng, H., Tan, W.W., et al: ‘MMU GASPFA: a COTS multimodal biometric database’, Pattern Recognit. Lett., 2013, 34, (15), pp. 20432050, doi: 10.1016/j.patrec.2013.01.027.
    9. 9)
      • 9. NIST BSSR1 biometric score set, Available at: www.nist.gov/itl/iad/image-group/nist-biometric-scores-set-bssr1.
    10. 10)
      • 10. Shang, D., Zhang, X., Han, J., et al: ‘MultiModal-database-XJTU: an available database for biometrics recognition with its performance testing’. IEEE 3rd Information Technology and Mechatronics Engineering Conf. (ITOEC), Chongqing, China, 2017, pp. 521526, doi: 10.1109/ITOEC.2017.8122351.
    11. 11)
      • 11. NIST FERET Face Recognition Technology, www.nist.gov/programs-projects/face-recognition-technology-feret.
    12. 12)
      • 12. Snelick, R., Uludag, U., Mink, A., et al: ‘Large-scale evaluation of multimodal biometric authentication using state-of-the-art systems’, IEEE Trans. Pattern Anal. Mach. Intell., 2005, 27, (3), pp. 450455.
    13. 13)
      • 13. Szczuko, P., Czyżewski, A., Hoffmann, P., et al: ‘Validating data acquired with experimental multimodal biometric system installed in bank branches’, J. Intell. Inf. Syst., 2017, pp. 131, doi: 10.1007/s10844-017-0491-2.
    14. 14)
      • 14. Czyżewski, A., Bratoszewski, P., Hoffmann, P., et al: ‘Performance analysis of developed multimodal biometric identity verification system’, Elektron. Konstrukcje, Technologie, Zastosowania, 2018, 59, (4), doi: 10.15199/13.2018.4.7.
    15. 15)
      • 15. Szczuko, P., Czyżewski, A., Szczodrak, M.: ‘Variable length sliding models for banking clients face biometry’, in ‘Multimedia tools and Applications’ (Springer, 2018), pp. 118, doi: 10.1007/s11042-018-6432-4, in print.
    16. 16)
      • 16. Greaves, F, Ramirez-Cano, D, Millett, C, et al: ‘Use of sentiment analysis for capturing patient experience from free-text comments posted online’, J. Med. Internet Res., 2013, 15, (11), p. 239. doi: 10.2196/jmir.2721.
    17. 17)
      • 17. Duan, W., Cao, Q., Yu, Y., et al: ‘Mining online user-generated content: using sentiment analysis technique to study hotel service quality’. 46th Hawaii Int. Conf. System Sciences, Wailea, Maui, HI, USA, 2013, pp. 31193128. doi: 10.1109/HICSS.2013.400.
    18. 18)
      • 18. Wang, H., Can, D., Kazemzadeh, A., et al: ‘A system for real-time Twitter sentiment analysis of 2012 U.S. Presidential election cycle’. Proc. ACL System Demonstrations, Jeju, Republic of Korea, 2012, pp. 115120.
    19. 19)
      • 19. Chen, W., Hong, Q., Li, X.: ‘GMM-UBM for text-dependent speaker recognition’. Int. Conf. Audio, Language and Image Processing, Shanghai, 2012, pp. 432435.
    20. 20)
      • 20. ALIZE, Open source recognition, University of Avignon. Available at http://mistral.univ-avignon.fr, accessed 21 January 2018.
    21. 21)
      • 21. McCool, C., Marcel, S., Hadid, A., et al: ‘Bi-Modal person recognition on a mobile phone: using mobile phone data’. IEEE ICME Workshop on Hot Topics in Mobile Mutlimedia, Melbourne, VIC, Australia, 2012.
    22. 22)
      • 22. Szczodrak, M., Czyżewski, A.: ‘Evaluation of face detection algorithms for the bank client identity verification’, Found. Comput. Decis. Sci., 2017, 42, (2), pp. 137148.
    23. 23)
      • 23. Bratoszewski, P., Czyżewski, A., Hoffmann, P., et al: ‘Pilot testing of developed multimodal biometric identity verification system’. Signal Processing, Algorithms, Architectures, Arrangements, and Applications, Poznań, Poland, 2017, pp. 184189. 20.9.2017-22.9.2017.
    24. 24)
      • 24. Bratoszewski, P., Czyżewski, A.: ‘Face profile view retrieval using time of flight camera image analysis’. Pattern Recognition and Machine Intelligence, PReMI 2015, Warsaw, Poland, 2015(LNCS, 9124).
    25. 25)
      • 25. Fujitsu Identity Management and PalmSecure. Available at https://www.fujitsu.com/au/Images/PalmSecure_Global_Solution_Catalogue.pdf, accessed 20 January 2018.
    26. 26)
      • 26. Na, J., Sui, H., Khoo, C., et al: ‘Effectiveness of simple linguistic processing in automatic sentiment classification of product reviews’. Conf. Int. Society for Knowledge Organization (ISKO), Wurzburg, Germany, November 2004, pp. 4954.
    27. 27)
      • 27. Na, J.-C., Khoo, C., Horng Jyh Wu, P.: ‘Use of negation phrases in automatic sentiment classification of product reviews’, Libr. Collect., Acquis., Tech. Serv., 2005, 29, (2), pp. 180191. https://doi.org/10.1016/j.lcats.2005.04.007.
    28. 28)
      • 28. Trstenjak, B., Mikac, S., Donko, D.: ‘KNN with TF-IDF based framework for text categorization’, Procedia Eng., 2014, 69, pp. 13561364. https://doi.org/10.1016/j.proeng.2014.03.129.
    29. 29)
      • 29. Ma, Z., Feng, J., Chen, L., et al: ‘An improved approach to terms weighting in text classification’. Int. Conf. Computer and Management (CAMAN), Wuhan, China, 19–21 May 2011.
    30. 30)
      • 30. Pawlak, Z.: ‘Rough sets theoretical aspects of reasoning about data’ (Kluwer, Dordrecht, 1991).
    31. 31)
      • 31. Pawlak, Z.: ‘Rough sets’, Int. J. Comput. Inf. Sci., 1982, 11, p. 341, https://doi.org/10.1007/BF01001956.
    32. 32)
      • 32. Bazan, J.G., Peters, J.F., Skowron, A.: ‘Behavioral pattern identification through rough set modelling’. Rough Sets, Fuzzy Sets, Data Mining, and Granular Gomputing. RSFDGrC, Springer, Berlin, Heidelberg, 2005(LNCS, 3642), pp. 688697.
    33. 33)
      • 33. Nguyen, S.H.: ‘On efficient handling of continuous attributes in large data bases’, Fundam. Inform., 2001, 48, (1), pp. 6181.
    34. 34)
      • 34. Janusz, A., Stawicki, S.: ‘Applications of approximate reducts to the feature selection problem’. Proc. Int. Conf. Rough Sets and Knowledge Technology (RSKT), Banff, Canada, vol. 6954, 2011, pp. 4550.
    35. 35)
      • 35. Janusz, A., Ślęzak, D.: ‘Random probes in computation and assessment of approximate reducts’. Proc. of RSEISP, Granada and Madrid, Spain, 2014(LNCS, 8537), pp. 5364.
    36. 36)
      • 36. Zhong, N., Dong, J., Ohsuga, S.: ‘Using rough sets with heuristics for feature selection’, J. Intell. Inf. Syst., 2001, 16, p. 199, doi: 10.1023/A:1011219601502.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-bmt.2018.5030
Loading

Related content

content/journals/10.1049/iet-bmt.2018.5030
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address