Advances in Biometric Person Authentication: International by Matti Pietikäinen, Abdenour Hadid (auth.), Stan Z. Li, PDF

By Matti Pietikäinen, Abdenour Hadid (auth.), Stan Z. Li, Zhenan Sun, Tieniu Tan, Sharath Pankanti, Gérard Chollet, David Zhang (eds.)

This publication constitutes the refereed lawsuits of the foreign Workshop on Biometric reputation platforms, IWBRS 2005, held in Beijing, China in October 2005 in the scope of ICCV 2005, the foreign convention on machine imaginative and prescient. This workshop combines the yearly chinese language convention on Biometric acceptance (Sinobiometrics 2005).

The 32 revised complete papers have been rigorously reviewed and chosen from a hundred thirty submissions. The papers tackle the issues of computerized and trustworthy authentication of people in face, iris, fingerprint, palmprint, speaker, writing and different biometrics, and give a contribution new principles to analyze and improvement of trustworthy and functional suggestions for biometric authentication.

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5% respectively. Since the MI values for all of the first 60 features are quite small, the proposed algorithm starts by picking up much the same features as AdaBoost. However, once the number of features increases, AdaBoost starts to pick highly redundant features. The improved recognition rate accuracy over AdaBoost caused by the use of features selected using the proposed algorithm proves the usefulness of our techniques in eliminating redundancy. 4 Algorithm Complexity and Determination of TMI Due to the introduction of mutual information, the proposed boosting algorithm required longer training time than that required by AdaBoost.

The Nature of Statistical Learning Theory. Springer-Verlag (1995) 3. : Fast training of support vector machines using sequential minimal optimization. In: Advances in Kernel Methods - Support Vector Learning. MIT Press (1999) 185–208 4. : Face recognition by support vector machines. Image and Vision Computing 19 (2001) 631–638 22 Manuele Bicego, Enrico Grosso, and Massimo Tistarelli 5. : Face recognition with support vector machines: Global versus component-based approach. In: Proc. Of IEEE Int.

38 Fei Yang et al. 5. C. Sanderson, K. K. Paliwal: Likelihood Normalization for Face Authentication in Variable Recording Conditions, Proc. IEEE International Conference on Image Processing (ICIP), Vol. I, pp. 301-304, Rochester, Sep. 2002. 6. F. Perronnin, J. L. Dugelay: Robust Score Normalization for Relational Approaches to Face Authentication, 12th European Signal Processing Conference, Sep. 2004. 7. J. Phillips, P. Grother, R. J. Micheals, D. M. Blackburn, E. Tabassi, J. M. Bone: Face Recognition Vendor Test 2002 Evaluation Report, NISTIR 6965, Mar.

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