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The well-known PCASYS system (Pattern-level Classi cation Automation System for Fingerprints) [73] developed by Candela et al from NIST is an hybrid system that uses neural network classi cation followed by an auxiliary classi er (pseudoridge tracer) used to improve the reliability of classi cation The directional image is rst registered with respect to the centre of the ngerprint image, then its dimensionality is reduced by PCA and classi ed by a probabilistic neural network (PNN) r Other Approaches Several systems are based on clustering or general-purpose classi ers Wang et al [76] use a k-means clustering algorithm coupled to a three-nearest neighbors classi er Cappelli et al [77] use a MKL classi er [78] applied to an enhanced version of the orientation image (see Figure 1414).

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These two loops are both conditional loops, but they work differently. The While loop evaluates its condition before executing the body of the loop, meaning that, if the condition is false before the loop begins, the loop never executes. Conversely, the Do-While loop evaluates its condition after the loop body, meaning that the loop body always executes at least once.

Another type of loop that CFSCRIPT carries over from CFML is the Structure loop, which loops over the top-level keys in a structure and looks as follows in CFML:

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Tan and Bhanu [79] propose an original approach based on feature-learning, where a set of arti cial features are learned and classi ed by a Bayesian classi er Genetic programming is used to discover evolved features that are obtained from combinations of primitive image processing operations r Combined Approaches Recently, some researchers [80 82] have proposed the combination of different approaches to exploit their complementarities..

<cfloop collection= #myStruct# item= theKey > <cfoutput>#theKey#: #myStruct[theKey]#<br></cfoutput> </cfloop>

0.024 0.024 0.020 0.020 0.0122 0.0122

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Jain et al. [83] adopt a two-stage classi cation strategy: A K-nearest neighbor classi er is used to nd the two most likely classes from a FingerCode feature vector; then a set of ten neural networks are trained to distinguish between each possible pair of classes. Yao et al. [84] use a combination of SVMs trained by two kind of features: the FingerCode representation of the ngerprint [85] and a distributed vectorial representation of the relational graph associated with the ngerprint obtained by Recursive Neural Networks. Cappelli et al. [86] improve the results obtained by [83] using a two-stage sequential architecture based on an MKL classi er to select the two-most-likely classes and a set of 10 SPD classi ers to discriminate between selected pair of classes. In references 67, 92, and 87, some classi ers based on the MKL transform are combined. In Table 14.7, some comparisons among different ngerprint classi cation methods are reported for the NIST DB4 [88]: Most of them were obtained by using the rst half of the database for training and the second half for testing. Furthermore, since DB4 contains an equal number of ngerprints for each class, some authors prefer to

The CFSCRIPT version is similar, as the following example shows:

for(theKey in myStruct) { WriteOutput( #theKey#: #myStruct[theKey]#<br> ); }

Table 14.7. Comparison Among Different Classi cation Approaches on DB4a Five Classes Method Candela et al. [73] Senior [71] Jain et al. [83] Cappelli et al. [89] Marcialis et al. [80] Yao et al. [82] Senior [81] Yao et al. [84] Cappelli and Maio [67] Tan et al. [79] Category Neural network Ridge line shape Combined Other Combined Combined Combined Combined Combined Other Equal 10.0% 7.9% 12.1% 10.7% 10% 7.0% 8.4% Weighted 7.0% 6.5% 9.6% 9.0% 8.1% 5.9% 8.3% Four Classes Equal 11.4% 5.2% 5.5% 6.9% 5.3% 4.7% 6.7% Weighted 6.1% 8.4% 5.1% 5.4% 6%

CFSCRIPT also has an equivalent to CFBREAK, as the following example shows:

for(i = 1; i LTE 10; i = i + 1) { if(RandRange(1,10) EQ 10) { break; } WriteOutput( #i#<br> ); }

0.074 0.086 0.029 0.041 0.0134 0.0244

a Classi cation error is reported for the ve-class and four-class problems. In the four-class problem, Arch

In this example, ColdFusion normally ends the loop after iterating ten times. If RandRange() ever returns 10 during the execution of this loop, however, the break statement ends the loop prematurely, immediately skipping past the end of the loop.

14

CFSCRIPT s continue statement instructs ColdFusion to skip the rest of the current loop iteration and immediately begin the next iteration of the loop, as shown in the following example:

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