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] ] ] ] ]

= = = = =

"prog"; "-d"; "-o"; "ofile"; "data0";

(6.5)

argv[0] is always set to the command being invoked The indexes 1 through argc-1 represent the actual options passed to the command Let's look at how we might extract and evaluate the command line options stored in argv In our example, we'll support the following usage:

program_name [-d] [-h] [-v] [-o output_file] [-l limit_value] file_name [ file_name [file_name [ ]]]

where s is a global translation of the graph and l denotes a local perturbation of the graph nodes. The choices of in Eq. (6.4) and of max in Eq. (6.5) control the rigidity/plasticity of the graph [7, 19]. Obviously, both functions (6.4) and (6.5) de ne a similarity measure between two faces. After the matching procedure, the distance Dt (r) is used as a quantitative measure for the similarity of two faces [7, 15, 19]. Figure 6.2 illustrates the graph matching procedure for a rectangular graph.

Anything in brackets is optional Thus, for example, a minimal command line simply indicates a file to process:

Alternative possible invocations include the following:

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Need for client certificate: EAP-TLS is designed for scenarios where server and client need to mutually authenticate each other and is specifically suited for cases where both server and client use certificates for this mutual authentication However, for scenarios where symmetric authentication methods are not required, eg when the client is not required to authenticate itself to the server or is not capable of using certificates to authenticate itself to the server, EAP-TLS has less applicability Requiring certificates from the user can be especially burdensome, since it adds to the load of certificate authorities (CA) and the complexity of certification and CRL creation processes User versus device certificates: EAP-TLS relies on client certificates for client authentications.

The facial region modeling is an important step of the EGM. Many image analysis techniques have been proposed in the literature for this purpose. The objective of the

The basic steps in handling the command line options are the following:

file:///F|/WinDDK/resources/CPPPrimer/c++primerhtm (347 / 1065) [2001-3-29 11:32:06]

6

1 Extract each option in turn from argv We'll use a for loop to do this, beginning our iteration at index 1 (thereby skipping the program name):

for ( int ix = 1; ix < argc; ++ix ) { char *pchar = argv[ ix ]; // }

2 Determine the type of option If it begins with a hyphen, then we know it is one of {h, d, v, l, o } Otherwise, it is either the actual limit value associated with -l, an output file name associated with -o, or the name of a file for the program to process We'll use a switch statement to determine whether a hyphen is present:

facial region modeling step is the construction of a feature jet at each node of the elastic graph. The extracted feature vectors should have certain properties in order to be: (a) Descriptive: The feature vectors should describe suf ciently the region around the graph node. (b) Robust: The feature vectors should be robust against geometrical transformations and illumination changes and should pose variations that are very common in facial biometrics. (c) Discriminative: The feature vectors should ideally be able to capture only or primarily the discriminative power of the region around the graph node. (d) Easily Computable: The computation cost for calculating the feature vector at each graph node should be low in order to be suitable for real-time applications. Toward the above objectives, many transformations have been proposed for extracting the feature vectors in EGM. In the remainder of this section the most representative image analysis tools that have been proposed for facial region modeling in EGM will be presented in more detail.

switch ( pchar[ 0 ] ) { case '-': { // recognize -h, -d, -v, -l, -o } default: { // handle limit value following -l // output file following -o // file names } }

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