New PDF release: Advances in Biometric Person Authentication: International

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

ISBN-10: 3540294317

ISBN-13: 9783540294313

This publication constitutes the refereed complaints of the overseas Workshop on Biometric attractiveness platforms, IWBRS 2005, held in Beijing, China in October 2005 in the scope of ICCV 2005, the foreign convention on laptop imaginative and prescient. This workshop combines the once a year chinese language convention on Biometric reputation (Sinobiometrics 2005).

The 32 revised complete papers have been rigorously reviewed and chosen from one 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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Extra info for Advances in Biometric Person Authentication: International Wokshop on Biometric Recognition Systems, IWBRS 2005, Beijing, China, October 22-23, 2005. Proceedings

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As a result of this, FloatBoosts computational requirements are approximately five times greater than that of AdaBoost. When the number of features is large (160,000+), as is normal in implementation scenarios utilising Gabor features, the FloatBoost training process could be unmanageable. A boosting algorithm, which is both effective in eliminating the non-effective classifiers and is computationally efficient, is required. We propose in this paper a novel approach to address this issue. The proposed boosting algorithm uses the idea of mutual information for redundancy elimination.

We propose in this paper a novel approach to address this issue. The proposed boosting algorithm uses the idea of mutual information for redundancy elimination. During the learning process, the mutual information between the candidate weak classifier and the selected weak classifiers is examined. As a result, the non-effective classifiers carrying information already captured by the selected feature/classifiers will be excluded. Since the mutual information is checked for only those candidate classifiers with small errors, the additional computation cost above that already required by AdaBoost is very low.

10, Oct. 2000. 9. Bo Cao, Shiguang Shan, Xiaohua Zhang, Wen Gao: Baseline Evaluations on the CASPEAL-R1 Face Database, SinoBiometrics 2004, pp. 370-378. 10. R. Brunelli, T. Poggio: Face Recognition: Features vs. Templates, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 15, No. 10, pp. 1042-1053, Oct. 1993. 11. M. Turk and A. Pentland: Eigenface for Recognition, Journal of Cognitive Neuroscience, Vol. 3, No. 1, pp. 71-86, 1991. 12. N. Belhumeur, J. P. Hespanha, D. J. Kriegman: Eigenfaces vs.

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Advances in Biometric Person Authentication: International Wokshop on Biometric Recognition Systems, IWBRS 2005, Beijing, China, October 22-23, 2005. Proceedings by Matti Pietikäinen, Abdenour Hadid (auth.), Stan Z. Li, Zhenan Sun, Tieniu Tan, Sharath Pankanti, Gérard Chollet, David Zhang (eds.)


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