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 foreign Workshop on Biometric popularity 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 yearly chinese language convention on Biometric acceptance (Sinobiometrics 2005).
The 32 revised complete papers have been conscientiously reviewed and chosen from a 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 rules to investigate and improvement of trustworthy and sensible recommendations 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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Extra info for Advances in Biometric Person Authentication: International Wokshop on Biometric Recognition Systems, IWBRS 2005, Beijing, China, October 22-23, 2005. Proceedings
In our algorithm, we use a large kernel low-pass-filter to estimate B (x, y). ˆ x, y) = LPF ( P ' ( x, y ) ) B （ (3) Once the B (x, y) has been estimated, we estimate the A (x, y). The Eq. (1) is equal to P ' ( x, y ) − B ( x, y ) = A( x, y ) P ( x, y ) (4) It is agreed that when in complicated environments, contrasting to the alteration of P (x, y), the alteration of A (x, y) is oppositely slow. A log transform can be applied to both size of Eq. (4), then a low-pass-filter is used to filter the P (x, y) which presents the high frequency, finally we get the estimation of A (x, y) as below.
Figure 4 and 5 show comparison results on the four probe sets of FERET and the six probe sets of CAS-PEAL respectively. For each classifier, the left column shows the EER by using UT, while the right column shows that of the Z-Norm method. From these figures, one can see that Z-Norm method can get better performance on FERET fafb, DupI, and DupII testing sets. On most of the testing sets of CAS-PEAL database, Z-Norm enhances the performance with only one exception (Fisherface on Aging test set).
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. IEEE Trans. com Abstract. The problem of illumination makes the face recognition still an unsolved problem.