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display in hexadecimal numeric base display in octal numeric base add fill characters to right of value add fill characters to left of value add fill characters between sign & value display floating point in decimal notation display floating point in scientific notation flush ostream buffer insert null then flush ostream buffer insert newline then flush ostream buffer "eat" white space fill white space with ch read or write value to w characters output integers in base b setprecision( n ) set floating point precision to n

scientific flush ends endl ws // these require #include <iomanip setfill( ch ) setw( w ) setbase( b )

5

indicates default stream state indicates default stream state indicates default stream state indicates default stream state indicates default stream state indicates default stream state indicates default stream state indicates default stream state

The iostream library is strongly typed The attempt, for example, to read from an ostream, or to assign an ostream to an istream, is both caught at compile-time and flagged as a type violation For example, given the following set of declarations,

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Karungaru et al [26] performed face recognition using template matching Template matching was performed using a genetic algorithm to automatically test several positions around the target and to adjust the size of the template as the matching process progressed The template was a symmetrical T-shaped region between the eyes, which covered the eyes, nose, and mouth Ozkan [27] used genetic algorithms for feature selection in face recognition In this work, the scale invariant feature transform (SIFT) [28] was used to extract features Since SIFT was originally designed for object recognition in general, genetic algorithms were used to identify SIFT features, which are more suitable to face recognition Huang and Weschler [29] developed an approach to identify eye location in face images using navigational routines, which were automated by learning and evolution using genetic algorithms.

#include <iostream> #include <fstream> class Screen; extern istream& operator>>( istream&, const Screen& ); extern void print( ostream& ); ifstream inFile;

The authenticator (NAS) sends an EAP request to the peer. This request can be a request for authentication credential or simply a request for some sort of information. The type field in EAP request message indicates what sort of information is being asked for. A prime example is asking for the user s identity. EAP requests carrying Identity type are usually called EAP Identity request messages. When human user interactions are required, optional displayable messages for the user may be included in the messaging. As noted in Table 10.1, if information exchange at this point is performed in clear-text form, exchanging user identity during this early phase is not recommended, especially if

the following two statements result in compile-time type violations:

int main() { Screen myScreen; // error: expects an ostream&

Speci cally, eye localization was divided into two steps: (i) the derivation of the saliency attention map and (ii) the possible classi cation of salient locations as eye regions The saliency map was derived using a consensus between navigation routines that were encoded as nite-state automata (FSA) exploring the facial landscape and evolved using GAs The classi cation stage was concerned with the optimal selection of features and the derivation of decision trees for con rmation of eye classi cation using genetic algorithms Sun and Yin [30] applied genetic algorithms for feature selection in 3D face recognition An individual face model was created from a generic model and two views of a face Genetic algorithms were used to select optimal features from a feature space composed of geometrical structures, the labeled curvature types of each vertex in the individualized 3D model Sun et al.

file:///F|/WinDDK/resources/CPPPrimer/c++primerhtm (1008 / 1065) [2001-3-29 11:32:15]

print( cin >> myScreen ); // error: expects >> operator inFile "error: output operator"; }

[31] approached the problem of gender classi cation using a genetic algorithm to select features A genetic algorithm was used to select a subset of features from a low-dimensional representation, which was obtained by applying PCA and removing eigenvectors that did not seem to encode information about gender As is evident from these citations, many feature-based approaches toward face recognition use genetic algorithms for feature selection However, these approaches employ a single feature space derived from a set of face images We believe that it is more effective to employ aimed at extracting person-speci c features and that an effective way to do this is by using genetic algorithms As observed by Turk et al [8], humans initially learn to recognize faces based on person-speci c characteristic features.

Input/output facilities are provided as a component of the C++ standard library 20 does not describe the entire iostream library in particular, the creation of user-defined manipulators and buffer classes are beyond the scope of a primer Instead, we have focused on the portion of the iostream library fundamental to providing program input/output

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