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

Cascaded multimodal biometric recognition framework

Cascaded multimodal biometric recognition framework

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Biometrics — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

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)
      • 1. Schapire, R.: ‘Strength of weak learnability’, Mach. Learn., 1990, 5, pp. 197227.
    2. 2)
      • 2. Freund, Y.: ‘Boosting a weak learning algorithm by majority’. Proc. Third Annual Workshop on Computational Learning Theory, 1990.
    3. 3)
      • 3. Breiman, L.: ‘Bagging predictors’, Mach. Learn., 1996, 24, (2), pp. 123140.
    4. 4)
      • 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.
    5. 5)
      • 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.
    6. 6)
      • 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).
    7. 7)
      • 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.
    8. 8)
      • 8. Iqbal, A., Namboodiri, A.M.: ‘Cascaded filtering for biometric identification using random projections’. Proc. 2011 National Conf. on Communication, 1–5, 2011.
    9. 9)
      • 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.
    10. 10)
      • 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.
    11. 11)
      • 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).
    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)
      • 13. Lakshmiprabha, N.S., Bhattacharya, J., Majumder, S.: ‘Face recognition using multimodal biometric features’. Int. Conf. on Image Information Processing, 2011.
    14. 14)
      • 14. Soviany, S., Soviany, C., Jurian, M.: ‘A multimodal approach for biometric authentication with multiple classifiers’, World Acad. Sci. Eng. Technol., 2011.
    15. 15)
      • 15. Baig, A., Nawaz, R.: ‘Cascaded face recognition system via sparse representation’. Proc. Ninth IEEE Int. Bhurban Conf. on Applied Science and Technology, 2012.
    16. 16)
      • 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.
    17. 17)
      • 17. Mahalanobis, P.C.: ‘On the generalised distance in statistics’. Proc. National Institute of Sciences of India, 1936, pp. 4955.
    18. 18)
      • 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).
    19. 19)
      • 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.
    20. 20)
      • 20. Niblack, W.: ‘An introduction to digital image processing’ (Prentice-Hall, Englewood Cliffs, NJ, 1986).
    21. 21)
      • 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.
    22. 22)
      • 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).
    23. 23)
      • 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).
    24. 24)
      • 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.
    25. 25)
      • 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.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-bmt.2012.0043
Loading

Related content

content/journals/10.1049/iet-bmt.2012.0043
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address