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By Enrique Argones Rúa, José Luis Alba Castro, Carmen García Mateo (auth.), Ben Schouten, Niels Christian Juul, Andrzej Drygajlo, Massimo Tistarelli (eds.)

This quantity constitutes the post-conference court cases of the 1st eu Workshop on Biometrics and identification administration, BIOID 2008, held in Roskilde, Denmark, in the course of might 7-9, 2008.

The 23 standard papers provided have been conscientiously chosen from quite a few submissions. They conceal the next issues: biometric info caliber; biometrical templates - face acceptance; biometrical templates -- different modalities; biometric assaults and countermeasures; biometric interfaces, and criteria and privateness. those papers are provided including four place papers by means of invited audio system on biometrics and id administration.

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Table 3. For each of the quality parameters a label in {“L: Low”, “M: Medium”, “H: High”} is used to characterize the level of “strictness” of the requirement in the specifications. “H” is used when the constraint is as “strict” as in the FBI IAFIS-IQS [3]; “M” and “L” are used when the specification is moderately or significantly relaxed, respectively, with respect to the corresponding FBI IAFIS-IQS. Parameter Acquisition area Output resolution accuracy Geometric accuracy2 Spatial frequency response3 Signal-to-noise ratio Fingerprint gray range 2 3 PIV IQS L L L M M M Level of “strictness” of the requirements PassDEÜV CNIPA-A CNIPA-B CNIPA-C M H M L H H M L H H M L H M L L H M L L H M L L CNIPA-A/B/C IQS set requirements on a slightly different measurement of geometric accuracy; however it can shown that PIV IQS is comparable to CNIPA-C requirement and PassDEÜV requirement (the same of the IAFIS IQS) is comparable to CNIPA-A requirement (see [1]).

6. 5 correlation coefficient ρ Fig. 7. Impact of the correlation ρ between the observed scores x and the observed quality measures qm, for multiplicative noise On Quality of Quality Measures for Classification 27 Table 2. 017 classifiers after 50 independent experimental runs in terms of mean Half Total Error Rate (HTER). The error bars show the standard deviation of HTER. Numerical data from this experiment are gathered in Table 2. 4 Conclusions We proposed a model of uncertain quality measurements of biometric signals, and we proved that the use of uncertain qm does not negatively impact class separation in respect to the baseline systems which do not use qm.

Table 2. The table reports, for each quality parameter, the characteristic of the scanners hypothesized for enrolment and verification. In fact, in a typical large-scale application, the scanner used during enrolment may be different from those used during verification. Note that “different” does not necessarily imply a distinct model/vendor: in fact, two scanners of the same model may produce different output images. For instance if a certain scanner model is compliant to a 500ppi±1% output resolution specification, one of such devices may work at 505ppi and another at 495ppi.

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