Advances in Biometric Person Authentication: 5th Chinese - download pdf or read online

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By Arun Ross, Anil K. Jain (auth.), Stan Z. Li, Jianhuang Lai, Tieniu Tan, Guocan Feng, Yunhong Wang (eds.)

ISBN-10: 3540240292

ISBN-13: 9783540240297

Following the former 4 annual meetings, the fifth chinese language convention on Biometrics popularity (Sinobiometrics 2004) used to be held in Guangzhou, China in December 2004. The convention this yr used to be aimed toward selling the overseas alternate of principles and delivering a chance for holding abreast of the newest advancements in biometric algorithms, structures, and purposes. the first Biometrics Verification pageant (BVC) on face, iris, and fingerprint attractiveness used to be additionally performed together with the convention. This booklet consists of seventy four papers awarded at Sinobiometrics 2004, contributed via researchers and business practitioners from Korea, Japan, Singapore, Hong Kong, France, united kingdom, US, in addition to China. of those, 60 papers have been chosen from a hundred and forty submissions and 14 have been invited. The papers not just awarded fresh technical advances, but in addition addressed concerns in biometric process layout, standardization, and purposes. integrated one of the invited have been 4 function papers at the rules and algorithms of the best-performing biometric engines, that have been both festival winners on the Face Authentication attempt (FAT) 2004 or the Fingerprint Verification festival (FVC) 2004, or they have been the best-performing iris and palmprint popularity algorithms. The papers have been complemented by means of 5 keynote lectures on biometrics, and face, fingerprint, and iris authentication and multimodal fusion through Arun Ross (West Virginia collage) and Anil ok. Jain (Michigan kingdom University), Josef Kittler (University of Surrey), John Daugman (University of Cambridge), Raffaele Cappelli (University of Bologna), and Stan Z. Li (Chinese Academy of Sciences).

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Additional resources for Advances in Biometric Person Authentication: 5th Chinese Conference on Biometric Recognition, SINOBIOMETRICS 2004, Guangzhou, China, December 13-14, 2004. Proceedings

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Match scores – irrespective whether these match scores are from the same individual or not. An estimate of is given by obtained by simply counting the that are smaller than and dividing by M. Expression 10 is a discrete function approximating the true F and is called the empirical distribution. ,M. , Now, it can be seen that of (10) where M is the number of samples that is used. 2 37 Bootstrap Confidence Intervals We have the set of match scores X and we want to have some idea as to how much importance should be given to That is, we are interested in a confidence region for in the form of quantiles of and are the and The Here, (which is a function of the data X), is the probability distribution of the estimate, called empirical distribution, The sample population X has distribution defined as which puts equal mass 1/M at each observation It should be noted that the samples in set X are the only data we have and the distribution (13) is the only distribution we have.

The Equal Error Rate. The Equal Error (EE) operating point, as shown in Figure 3, leads us to the Equal Error Rate (EER) which is an obvious, simple choice to judge the quality of a matcher. The Equal Error point of a biometric matcher is the operating point at the intersection of the line FMR = FNMR with the ROC of the matcher. The equal error rate is the value of the error rates at this point The EER (subject to statistical significance [1]) can certainly tell us that one matcher is better than another, but only does so for a narrow range of operating points in the neighborhood of the EER operating point.

Daugman irises of N clones. Although eye colour is of course strongly determined genetically, as is overall iris appearance, the detailed patterns of genetically identical irises appear to be as uncorrelated as they are among unrelated eyes. Using the same methods as described above, 648 right/left iris pairs from 324 persons were compared pairwise. 031, and their distribution (Fig. 6) was statistically indistinguishable from the distribution for unrelated eyes (Fig. 4). 507) expected for unrelated eyes.

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Advances in Biometric Person Authentication: 5th Chinese Conference on Biometric Recognition, SINOBIOMETRICS 2004, Guangzhou, China, December 13-14, 2004. Proceedings by Arun Ross, Anil K. Jain (auth.), Stan Z. Li, Jianhuang Lai, Tieniu Tan, Guocan Feng, Yunhong Wang (eds.)

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