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 court cases of the overseas Workshop on Biometric reputation structures, IWBRS 2005, held in Beijing, China in October 2005 in the scope of ICCV 2005, the overseas convention on desktop imaginative and prescient. This workshop combines the once a year chinese language convention on Biometric attractiveness (Sinobiometrics 2005).
The 32 revised complete papers have been rigorously reviewed and chosen from a hundred thirty submissions. The papers handle 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 sensible strategies for biometric authentication.
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Extra resources for Advances in Biometric Person Authentication: International Wokshop on Biometric Recognition Systems, IWBRS 2005, Beijing, China, October 22-23, 2005. Proceedings
H ( X ) can be defined as below: H(X ) = − ∑ p( X = x) lg( p( X = x)) x (1) Mutual information I (Y ; X ) is a measure of general interdependence between two random variables X and Y : I (Y ; X ) = H ( X ) + H (Y ) − H ( X , Y ) (2) Using Bayes rule on conditional probabilities, Equation 2 can be rewritten as: I (Y ; X ) = H ( X ) − H ( X | Y ) = H (Y ) − H (Y | X ) (3) Since H (Y ) measures the priori uncertainty of Y and H (Y | X ) measures the conditional posteriori uncertainty of Y after X is observed, the mutual information I (Y ; X ) measures how much the uncertainty of Y is reduced if X has been observed.
The algorithm focuses on difficult training patterns, increasing their representation in successive training sets. Over a number of T rounds, T weak classifiers are selected to form the final strong classifier. In each of the iterations, the space of all possible weak classifiers is searched exhaustively to find the one with the lowest weighted classification error. This error is then used to update the weights such that the wrongly classified samples get their weights increased. The resulting strong clas- Gabor Feature Selection for Face Recognition Using Improved AdaBoost Learning 41 sifier is a weighted linear combination of all T selected weak classifiers.
We aim to prove the tolerance of our system against the localized in the future. Currently, we are working on designing an adaptive TMI determination algorithm to improve the proposed boosting algorithm further. Reference 1. Y. Freund and R. Schapire, "A decision-theoristic generalization of on-line learning and an application to boosting," Journal of Computer and System Sciences, vol. 55, no. 1, pp. 119139, 2000. 2. P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features," in Proc.
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.)