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To quickly remove all elements from a structure, use StructClear(), as follows:

example shows:

There are several reasons why learning-based methods might overcome traditional techniques [5]; in fact, they allow us to r Extract hidden relationships and correlations among the data r Automatically select the most discriminant features r Deal with a large amount of data that cannot be effectively encoded by humans r Take into account characteristics not completely known at design time On the other hand, learning-based techniques suffer from two main problems: (1) A representative labeled training set is necessary to provide ground truth information to the system during the learning stage; (2) the risk of overtraining the systems (i.e., over tting the data) is always present and could lead to a system that works very well on the training set but whose performance signi cantly degrades on unseen data. This chapter reviews the main learning-based approaches to ngerprint recognition; the chapter is organized in ve subchapters, each focusing on a particular ngerprint recognition subproblem as shown in Table 14.1. Finally, Section 14.7 draws some conclusions and points out some promising research directions for the future.

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To get a specific count of how many top-level (that is, nonnested) elements are in a structure, use StructCount(), as follows:

Figure 22.21 Acceptable and marginally acceptable levels of risk potted on F-N chart. (T. W. Lambe & Associates 1982, 1989, reproduced with permission.)

To tell whether a given variable contains a structure, use IsStruct(), as follows:

In this step a ngerprint image is acquired through a given sensor. Apart from the hardware and software technologies used for on-line ngerprint acquisition, two important processing steps are strictly related to this phase: quality check and liveness detection. Quality check allows to control the quality of the acquired sample and to reject it in case of insuf cient quality; liveness detection is aimed at detecting the presentation of fake ngerprints.

<cfif IsStruct(myStruct)> IsStruct returns TRUE if the passed variable is a structure and FALSE if it does not.

The performance of a ngerprint recognition system is heavily affected by the ngerprint image quality. Several factors determine the quality of a ngerprint image: skin conditions, sensor quality and conditions, user cooperation and proper use of the sensing device (see Figure 14.1). Fingerprint quality is usually de ned as a measure of the clarity of ridge and valley pattern, but it is not simple to describe it with mathematical equations. Machine learning techniques can then be exploited to train systems by examples. A taxonomy of existing approaches for ngerprint image quality computation is reported in reference 6, where they are divided into three families: (i) approaches based on local features, (ii) approaches based on global features, and (iii) approaches addressing the problem of quality assessment as a classi cation problem. Methods belonging to the third class make large use of learning techniques [7, 8]. The method proposed by NIST [7] operationally de nes the quality as a prediction of a matcher performance: Good-quality ngerprints are likely to produce high match scores. An important advantage of this method is that it does not require a ground truth provided by a human expert; in fact, de ning the ground truth by visual inspection is quite complicated, could lead to subjective evaluations, and is not necessarily the best approach when the focus is on automatic matching algorithms. Given a training data set D containing n different ngers x1 ,. . . , xn and 2 samples for each nger (the second sample of the nger xi is here denoted as xi ), the normalized matching score of xi is de ned as normscore(xi ) = score(xi , xi ) avgj=1...n,j = i (score(xj , xj )) / stdj=1...n,j = i (score(xj , xj )) / , (14.1)

One form of CFLOOP enables you to loop over the keys in a structure, as the following example shows:

To understand the risk faced by the tank farm owners, the calculated exceedance probabilities and corresponding consequences were plotted in a probability-consequence or F-N

<cfoutput> <cfloop collection= #myStruct# item= theItem > The value of the #theItem# key is #myStruct[theItem]#.<br> </cfloop> </cfoutput>

where score(a, b) returns the matching score between the two ngerprints a and b according to a given automatic matcher, avg() is the average value, and std() is

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