Download e-book for iPad: Advances in Biometric Person Authentication: International by Matti Pietikäinen, Abdenour Hadid (auth.), Stan Z. Li,

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

The 32 revised complete papers have been conscientiously reviewed and chosen from one hundred thirty submissions. The papers deal with the issues of automated 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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Read Online or Download Advances in Biometric Person Authentication: International Wokshop on Biometric Recognition Systems, IWBRS 2005, Beijing, China, October 22-23, 2005. Proceedings PDF

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Additional 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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In: Advances in Neural Information Processing Systems. Volume 12. (1999) 526–532 11. : Support vector domain description. Pattern Recognition Letters 20 (1999) 1191–1199 12. : Face Detection Using One-Class-Based Support Vectors. In: Proc. of Sixth IEEE Int. Conf. on Automatic Face and Gesture Recognition (2004) 457–463 13. : Rapid object detection using a boosted cascade of simple features. In: IEEE Proc. Int. Conf. on Computer Vision and Pattern Recognition. Volume 1. (2001) 511–518 14. : Support vector machines for 3d object recognition.

39–49, 2005. © Springer-Verlag Berlin Heidelberg 2005 40 Linlin Shen et al. combined strong classifier and the results analyzed for improvements in the classification error rate. 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.

Volume 1. (2000) 29–32 9. : Support vector clustering. Journal of Machine Learning Research 2 (2001) 125–137 10. : Support vector method for novelty detection. In: Advances in Neural Information Processing Systems. Volume 12. (1999) 526–532 11. : Support vector domain description. Pattern Recognition Letters 20 (1999) 1191–1199 12. : Face Detection Using One-Class-Based Support Vectors. In: Proc. of Sixth IEEE Int. Conf. on Automatic Face and Gesture Recognition (2004) 457–463 13. : Rapid object detection using a boosted cascade of simple features.

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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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