Advances in Biometric Person Authentication: 5th Chinese by Arun Ross, Anil K. Jain (auth.), Stan Z. Li, Jianhuang Lai,

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

Following the former 4 annual meetings, the fifth chinese language convention on Biometrics acceptance (Sinobiometrics 2004) used to be held in Guangzhou, China in December 2004. The convention this 12 months used to be geared toward selling the overseas trade of principles and delivering a chance for retaining abreast of the newest advancements in biometric algorithms, platforms, and functions. the first Biometrics Verification festival (BVC) on face, iris, and fingerprint attractiveness was once additionally carried out together with the convention. This e-book 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 offered contemporary technical advances, but additionally addressed concerns in biometric method layout, standardization, and functions. incorporated 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 through 5 keynote lectures on biometrics, and face, fingerprint, and iris authentication and multimodal fusion via Arun Ross (West Virginia college) 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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Extra 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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Classical books like [16] warn for these pitfalls. Performance Evaluation in 1:1 Biometric Engines 41 All these database are from samples obtained from a finite number of volunteer subjects. As long as all this data is considered to be acquired from the user population of the biometric application, this data is a closed database of samples. When these databases are also used to generate false match error conditions, the associated error estimates like the FAR and FNR are socalled “zero-effort” error estimates, as introduced by the signature research community [1].

Daugman diameters of 11mm and 5mm respectively, the observed amount of statistical variability among different iris patterns corresponds to an information density of about on the iris. The theoretical binomial distribution plotted as the solid curve in Fig. g. coin tosses that are “heads” in each run). In our case, is the HD, the fraction of phase bits that happen to agree when two different irises are compared. To validate such a statistical model we must also study the behaviour of the tails, by examining quantile-quantile plots of the observed cumulatives versus the theoretically predicted cumulatives from 0 up to sequential points in the tail.

3]) discussed in Section 4. A matcher operating at a high threshold T has a low FMR but high FNMR; a low threshold, conversely, means high FMR and low FNMR. The cumulative probability distributions and of the non-match scores and match scores respectively are defined as follows: These can be used interchangeably with FMR and FNMR because there is 1-1 correspondence as shown in Figure 2. M. K. Ratha, and S. Pankanti Fig. 2. 1 and Definitions of FAR and FRR, Positive Authentication Positive verification (“I am who I claim I am”) systems can make two kinds of errors, a False Accept or a False Reject: I.

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