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 popularity (Sinobiometrics 2004) was once held in Guangzhou, China in December 2004. The convention this yr was once aimed toward selling the foreign trade of principles and delivering a chance for conserving abreast of the newest advancements in biometric algorithms, structures, and functions. the first Biometrics Verification festival (BVC) on face, iris, and fingerprint attractiveness used to be additionally carried out at the side of the convention. This ebook consists of seventy four papers offered at Sinobiometrics 2004, contributed through 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 one hundred forty submissions and 14 have been invited. The papers not just provided fresh technical advances, but additionally addressed concerns in biometric process layout, standardization, and purposes. incorporated one of the invited have been 4 function papers at the principles and algorithms of the best-performing biometric engines, which have been both pageant winners on the Face Authentication try (FAT) 2004 or the Fingerprint Verification pageant (FVC) 2004, or they have been the best-performing iris and palmprint popularity algorithms. The papers have been complemented by way of 5 keynote lectures on biometrics, and face, fingerprint, and iris authentication and multimodal fusion by way of Arun Ross (West Virginia college) and Anil ok. Jain (Michigan country 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 info for Advances in Biometric Person Authentication: 5th Chinese Conference on Biometric Recognition, SINOBIOMETRICS 2004, Guangzhou, China, December 13-14, 2004. Proceedings
Expressing this variation as a discrimination entropy  and using typical iris and pupil 14 J. 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.
33 for the empirical data sets shown can perfectly separate the dual distributions. At this criterion, using the cumulatives of (11) in Table 1, the theoretical false match probability is 1 in 4 million. 24 J. Daugman Notwithstanding this diversity among iris patterns and their apparent singularity because of so many dimensions of random variation, their utility as a basis for automatic personal identification would depend upon their relative stability over time. g. , ) as medical fraud.
031, and their distribution (Fig. 6) was statistically indistinguishable from the distribution for unrelated eyes (Fig. 4). 507) expected for unrelated eyes. It appears that the phenotypic random patterns visible in the human iris are almost entirely epigenetic . 5 Recognizing Irises Regardless of Size, Position, and Orientation Robust representations for pattern recognition must be invariant to changes in the size, position, and orientation of the patterns. In the case of iris recognition, this means we must create a representation that is invariant to the optical size of the iris in the image (which depends upon the distance to the eye, and the camera optical magnification factor); the size of the pupil within the iris (which introduces a non-affine pattern deformation); the location of the iris within the image; and the iris orientation, which depends upon head tilt, torsional eye rotation within its socket (cyclovergence), and camera angles, compounded with imaging through pan/tilt eye-finding mirrors that introduce additional image rotation factors as a function of eye position, camera position, and mirror angles.