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.)

This ebook constitutes the refereed lawsuits of the foreign Workshop on Biometric popularity structures, 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 once a year chinese language convention on Biometric reputation (Sinobiometrics 2005).

The 32 revised complete papers have been conscientiously 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 rules to investigate and improvement of trustworthy and functional options 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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The data acquisition module acquires the biometric data from a user; the feature extraction module processes the acquired biometric data and extracts a feature set to represent it; the matching module compares the extracted feature set with the stored templates using some matching algorithm in order to calculate matching scores; finally, the decision module compares the matching scores with a threshold. If the score is equal or larger than the threshold, the claim is conformed, or else is denied.

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. In: IEEE Proc. Int. Conf. on Computer Vision and Pattern Recognition.

So, finally we have: p (θi | λi ) = K, p (θi | λi ) (i = 1, 2," | G |) . (8) If we further assume the genuine scores of subject Gi satisfy a Gaussian distribution N ( µi , σ i ) and imposter scores be a Gaussian distribution N ( µi , σ i ) , given a certain value K , the subject specific threshold can be solved by θi = f ( K , µi ,σ i , µi ,σ i ) . 2 Score Normalization (SN) Previous studies have shown that the performance of a number of biometric verification systems, especially those based on behavioral traits such as written signature or voice, can be improved by using score normalization method.

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