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 e-book 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 popularity (Sinobiometrics 2005).

The 32 revised complete papers have been rigorously reviewed and chosen from a 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 investigate and improvement of trustworthy and functional recommendations 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 resources 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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Rank Constrained Recognition under Unknown Illuminations. Proceeding of AMFG, 2003. 6. Jiali Zhao, Yong Su,Dejun Wang. Illumination ratio image: synthesizing and recognition with varying illuminations. Pattern Recognition Letter 2003(24):2703-2710. 7. Jonathon Philips and Yehuda Vardi. Efficient illumination normalization of facial images. Pattern Recognition Letter 1996(17):921-927. 8. Haitao Wang, Hong Wei and Yangsheng Wang. Face Representation under Different Illumination Conditions. Proceeding of ICME, 2003.

48 Linlin Shen et al. Whilst the selected features/classifiers are non-redundant and thus achieve higher accuracy for classification, the computation burden added to AdaBoost is very low. 1 times longer than that of AdaBoost. It should also be noted that though the face images are normalized manually in this paper, the work in [21] proved the robustness of non-redundant features against the mis-alignment of automatic face detection system. We aim to prove the tolerance of our system against the localized in the future.

N. Belhumeur, J. P. Hespanha, D. J. Kriegman: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 19, Issue 7, Jul. 1997. R. cn Abstract. Though AdaBoost has been widely used for feature selection and classifier learning, many of the selected features, or weak classifiers, are redundant. By incorporating mutual information into AdaBoost, we propose an improved boosting algorithm in this paper. The proposed method fully examines the redundancy between candidate classifiers and selected classifiers.

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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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