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The request message carries a challenge, while the response either carries a reply to challenge (type 4) or a NAK (type 3) if the peer does not accept the MD5-challenge as authentication mechanism. Type value 5 indicates that messaging relates to one-time passwords (OTPs). We explained OTPs in 2. Here, the request contains an OTP challenge, while the response includes a reply. Type value 6 indicates that messaging relates to authentication method using a generic token card implementation that requires user input. The request contains a displayable message and the response contains the token card information necessary for authentication. In the remainder of this chapter we will explain how some of the more sophisticated authentication methods work. However, before doing that it is useful to go through how the EAP framework is used to carry a generic authentication method both in order to understand the remainder of the chapter better and to help the reader with her own design of future EAP authentication methods.

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advances the iterator to address the next element (We look at iterators, vectors, and the general STL idiom in considerable detail in 6) One caution is to not mix the two usage idioms For example, the definition

vector<int> ivec;

Figure 5.10. Sample face images with varying pose and illumination from the FacePix (30)

ivec[0] = 1024;

is an error, because there is as yet no first element We can index only elements that are present within the vector The size() operation returns the number of elements a vector contains Similarly, when we define a vector of a given size, as in

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We selected at random two images out of each set of three frontal (0 ) (Figure 5.11) images for training, and used the remaining image for testing. The genetic algorithms used the training images to nd a set of Gabor feature detectors that were able to distinguish each person s face from all of the other people in the training set. These feature detectors were then used to recognize the test images. In order to evaluate the performance of our system, we used the same set of training and testing images with face classi cation algorithm based on low-dimensional representation of face images extracted through principal component analysis [34]. Speci cally, the performance of the implementation of PCA-based face recognition followed by reference 35 was used in our experiments.

vector<int> ia( 10 );

any insertions add to the size of the vector rather than write over the existing elements That may seem obvious; however, the following is not an uncommon beginner mistake:

const int size = 7; int ia[ size ] = { 0, 1, 1, 2, 3, 5, 8 }; vector< int > ivec( size ); for ( int ix = 0; ix < size; ++ix )

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Each Gabor feature corresponds to a particular Gabor wavelet (i.e., a particular special frequency, a particular orientation, and a particular Gaussian-de ned spatial extent) applied to a particular (x, y) location within a normalized face image. (Given that 125 different Gabor lters were generated, by varying , , and in 5 steps each, and given that each face image contained 128 128 = 16, 384 pixels, there was a pool of 125 16384 = 2, 048, 000 potential Gabor features to choose from.) We used an Ndimensional vector to represent each person s face in the database, where N represents the predetermined number of Gabor features that the genetic algorithm (GA) selected from this pool. Figure 5.12 shows an example face image, marked with ve locations where Gabor features will be extracted (i.e., N = 5). Given any normalized face image, real number Gabor features are extracted at these locations using Eq. (5.6). This process can be envisioned as a projection of a 16,384-dimensional face image onto an N-dimensional subspace, where each dimension is represented by a single Gabor feature detector. Thus, the objective of the proposed methodology is to extract an N-dimensional real-valued person-speci c feature vector to characterize each person in the database. The N (x, y) locations (and the spatial frequency and spatial extent parameters of the N Gabor wavelets used at these locations) are chosen by a GA, with a tness function that takes into account the ability of each Gabor feature detector to distinguish one face from all the other faces in the database.

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