By Matti Pietikäinen, Abdenour Hadid (auth.), Stan Z. Li, Zhenan Sun, Tieniu Tan, Sharath Pankanti, Gérard Chollet, David Zhang (eds.)
This e-book constitutes the refereed lawsuits of the overseas Workshop on Biometric acceptance platforms, 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 yearly 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 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 useful options for biometric authentication.
Read 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
However, as the whole, the two result images still have the same distribution and their histograms should be similar. This suggests that we can use the statistical method such as expectation to make the combination rational. A Novel Illumination Normalization Method for Face Recognition 27 Fig. 1. The frame procedure of our proposal illumination invariant method w h Pa = ∑ ∑ Pa (i , j ) (10) i=0 j =0 w h Pi = ∑∑ Pi (i, j) (11) i =0 j =0 We define r as follow. (11). P ' ( x, y ) = Pa ( x, y ) + r × Pi ( x, y ) 1+ r (13) Experiments shows that it can eliminate the influences that come from the different bar centers and makes the combination of the two algorithms more rational.
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. Fisherfaces: Recognition Using Class Specific Linear Projection, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 19, Issue 7, Jul.
Gabor Feature Selection for Face Recognition Using Improved AdaBoost Learning 47 Fig. 6. Recognition performance Table 1. e. the faster training speed. Actually AdaBoost can be seen as a special case of our algorithm when the value of TMI is set as 1. In this case, the features, or weak classifiers selected by the proposed algorithm will be exactly the same as those chosen by AdaBoost. The value of TMI needs to be selected appropriately to make sure that selected features are both non-redundant and useful for classification.
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.)