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 reputation (Sinobiometrics 2004) was once held in Guangzhou, China in December 2004. The convention this yr was once geared toward selling the foreign alternate of principles and supplying a chance for maintaining abreast of the newest advancements in biometric algorithms, platforms, and functions. the first Biometrics Verification festival (BVC) on face, iris, and fingerprint acceptance was once additionally performed together with the convention. This booklet consists of seventy four papers awarded at Sinobiometrics 2004, contributed via researchers and commercial 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 contemporary technical advances, but additionally addressed concerns in biometric process layout, standardization, and functions. incorporated one of the invited have been 4 characteristic papers at the rules and algorithms of the best-performing biometric engines, that have been both pageant winners on the Face Authentication try (FAT) 2004 or the Fingerprint Verification festival (FVC) 2004, or they have been the best-performing iris and palmprint attractiveness algorithms. The papers have been complemented through 5 keynote lectures on biometrics, and face, fingerprint, and iris authentication and multimodal fusion through Arun Ross (West Virginia collage) and Anil okay. Jain (Michigan nation 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 info for Advances in Biometric Person Authentication: 5th Chinese Conference on Biometric Recognition, SINOBIOMETRICS 2004, Guangzhou, China, December 13-14, 2004. Proceedings
Fig. 7. Scenario evaluation where volunteers are drafted to use several biometric systems on a regular basis. M. K. Ratha, and S. Pankanti A group of volunteer subjects is drafted, and this group uses the system periodically over some duration (ideally months or even years to capture the temporal variability of the biometrics) while statistics are collected. Many such evaluations will try to compare different biometrics or different vendors’ technologies at the same time, so the test facility will contain all the systems being tested, and each volunteer will generally use all of them in sequence.
The solid curve in Fig. 7 is a plot of (11), incorporating (4) and (8) as its terms, and it shows an excellent fit between theory (binomial extreme value sampling) and data. 33 illustrates the extreme improbability that the phase sequences for two different irises might disagree in fewer than a third of their bits. This suggests that in order to identify people by their iris patterns with high confidence, we need to demand only a very forgiving degree of match (say, Recognising Persons by Their Iris Patterns 6 19 Uniqueness of Failing the Test of Statistical Independence The statistical data and theory presented above show that we can perform iris recognition successfully just by a test of statistical independence.
Consequently, a matcher can make two types of errors ( ). (i) False match (FM) error occurs when score and matcher engine erroneously decides when indeed is true (also known as Type I error; and (ii) False non-match (FNM) error occurs when score and matcher engine decides when indeed is true (also known as Type II error). The False Match Rate (FMR) and the False Non-Match Rate (FNMR) are then the frequencies at which FM and FNM occur, respectively. The FMR is the area of the hatched region on the right under the non-match (mismatch) score density curve the proportion of the time when is indeed true: Similarly, Unfortunately, for biometric applications, the non-match score distribution and the match score distribution in Figure 1 always overlap, so it is not possible to choose a threshold for which both FMR = 0 and FNMR = 0.
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.)