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access icon free Cascaded multimodal biometric recognition framework

A practically viable multi-biometric recognition system should not only be stable, robust and accurate but should also adhere to real-time processing speed and memory constraints. This study proposes a cascaded classifier-based framework for use in biometric recognition systems. The proposed framework utilises a set of weak classifiers to reduce the enrolled users’ dataset to a small list of candidate users. This list is then used by a strong classifier set as the final stage of the cascade to formulate the decision. At each stage, the candidate list is generated by a Mahalanobis distance-based match score quality measure. One of the key features of the authors framework is that each classifier in the ensemble can be designed to use a different modality thus providing the advantages of a truly multimodal biometric recognition system. In addition, it is one of the first truly multimodal cascaded classifier-based approaches for biometric recognition. The performance of the proposed system is evaluated both for single and multimodalities to demonstrate the effectiveness of the approach.

References

    1. 1)
    2. 2)
      • 25. Hornak, L.A., Ross, A., Crihalmeanu, S.G., Schuckers, S.A.: ‘A protocol for multibiometric data acquisition storage and dissemination’. Problem/Technical Report, West Virginia University, 2007.
    3. 3)
      • 4. Kearns, M., Valiant, L.G.: ‘Learning Boolean formulae or finite automata is as hard as factoring’. Technical Report TR-14-88, Harvard University Aiken Computation Laboratory, August1988.
    4. 4)
    5. 5)
    6. 6)
    7. 7)
      • 20. Niblack, W.: ‘An introduction to digital image processing’ (Prentice-Hall, Englewood Cliffs, NJ, 1986).
    8. 8)
      • 24. Chikkerur, S., Cartwright, A.N., Govindaraju, V.: ‘K-plet and coupled BFS: a graph based fingerprint representation and matching algorithm’. Int. Conf. on Biometrics, 2006, pp. 309315.
    9. 9)
      • 16. Baig, A., Bouridane, A., Kurugollu, F.: ‘A novel modality independent score-level quality measure’. IEEE, IET Int. Symp. on Communication Systems, Networks and Digital Signal Processing, 2010.
    10. 10)
      • 4. Kearns, M., Valiant, L.G.: ‘Learning Boolean formulae or finite automata is as hard as factoring’. Technical Report TR-14-88, Harvard University Aiken Computation Laboratory, August1988.
    11. 11)
      • 22. Daugman, J.G.: ‘High confidence visual recognition of persons by a test of statistical independence’, IEEE Trans. Pattern Anal. Mach. Intell., 1993, 15, (11), pp. 11481161 (doi: 10.1109/34.244676).
    12. 12)
      • 12. Lee, P.H., Chu, L.J., Huang, Y.P., Shih, S.W., Chen, C.S., Wang, H.M.: ‘Cascading multimodal verification using face, voice and iris information’. IEEE Int. Conf. on Multimedia and Exp., 2007, pp. 847850.
    13. 13)
      • 17. Mahalanobis, P.C.: ‘On the generalised distance in statistics’. Proc. National Institute of Sciences of India, 1936, pp. 4955.
    14. 14)
      • 19. Baig, A., Bouridane, A., Kurugollu, F.: ‘A corner strength based fingerprint segmentation algorithm with dynamic thresholding’. Proc. 19th Int. Conf. on Pattern Recognition (ICPR), 2008.
    15. 15)
      • 11. Erzin, E., Yemez, Y., Takelp, M.A.: ‘Multimodal speaker identification using an adaptive classifier cascade based on modality reliability’, IEEE Trans. Multimed., 2005, 7, (5), pp. 840852 (doi: 10.1109/TMM.2005.854464).
    16. 16)
      • 25. Hornak, L.A., Ross, A., Crihalmeanu, S.G., Schuckers, S.A.: ‘A protocol for multibiometric data acquisition storage and dissemination’. Problem/Technical Report, West Virginia University, 2007.
    17. 17)
      • 2. Freund, Y.: ‘Boosting a weak learning algorithm by majority’. Proc. Third Annual Workshop on Computational Learning Theory, 1990.
    18. 18)
      • 1. Schapire, R.: ‘Strength of weak learnability’, Mach. Learn., 1990, 5, pp. 197227.
    19. 19)
      • 23. Masek, L., Kovesi, P.: ‘MATLAB source code for a biometric identification system based on iris patterns’ (The School of Computer Science and Software Engineering, The University of Western Australia, 2003).
    20. 20)
      • 21. Tico, M., Kuosmanen, P.: ‘New approach of automated fingerprint matching’. Proc. SPIE Symp. on Electronic Imaging Systems and Image Processing Methods, Real-Time Imaging V, San Jose, California, USA, 20–26 January 2001, vol. 4303, pp. 115126.
    21. 21)
      • 6. Xu, L., Krzyzak, A., Suen, C.Y.: ‘Methods of combining multiple classifiers and their applications to handwriting recognition’, IEEE Trans. Syst. Man Cybern., 1992, 22, pp. 418435 (doi: 10.1109/21.155943).
    22. 22)
      • 7. Sun, Z., Wang, Y., Tan, T., Cui, J.: ‘Improving iris recognition accuracy via cascaded classifiers’. Proc. First Int. Conf. on Biometric Authentication, (LNCS3072), 2004, pp. 418425.
    23. 23)
      • 14. Soviany, S., Soviany, C., Jurian, M.: ‘A multimodal approach for biometric authentication with multiple classifiers’, World Acad. Sci. Eng. Technol., 2011.
    24. 24)
      • 10. Praviz, M., Moin, M.S.: ‘Boosting approach for score level fusion in multimodal biometrics based on AUC maximization’, J. Inf. Hiding Multimed. Signal Process., 2011, 2, (1), pp. 5159.
    25. 25)
      • 13. Lakshmiprabha, N.S., Bhattacharya, J., Majumder, S.: ‘Face recognition using multimodal biometric features’. Int. Conf. on Image Information Processing, 2011.
    26. 26)
      • 8. Iqbal, A., Namboodiri, A.M.: ‘Cascaded filtering for biometric identification using random projections’. Proc. 2011 National Conf. on Communication, 1–5, 2011.
    27. 27)
      • 5. Viola, P., Jones, M.J.: ‘Rapid object detection using a boosted cascade of simple features’. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, 2001.
    28. 28)
      • 15. Baig, A., Nawaz, R.: ‘Cascaded face recognition system via sparse representation’. Proc. Ninth IEEE Int. Bhurban Conf. on Applied Science and Technology, 2012.
    29. 29)
      • 3. Breiman, L.: ‘Bagging predictors’, Mach. Learn., 1996, 24, (2), pp. 123140.
    30. 30)
      • 9. Sigari, M.H., Pourshahabi, M.R., Pourreza, H.R.: ‘An ensemble classifier approach for static signature verification based on multiple-resolution extracted features’, Int. J. Signal Process., Image Process. Pattern Recognit., 2012, 5, (1), pp. 2136.
    31. 31)
      • 18. Shi, Z., Govindaraju, V.: ‘A chaincode based scheme for fingerprint feature extraction’, Pattern Recognit. Lett., 2006, 27, pp. 462468 (doi: 10.1016/j.patrec.2005.09.003).
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