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A face detection algorithm, based on Adaboost [37], is then used to (a) identify the frames of the video where a face is present and (b) localize that face within that frame This detected face is then cropped and compared to indexed faces in a face database The performance of the proposed approach for identifying person-speci c features relies, to a large extent, on obtaining near-frontal views of faces To offset this limitation, there is ongoing work [38] to perform person-independent head pose estimation on the face images obtained from this platform It is expected that this will help us select face images from the video stream with near-frontal views, which will improve the performance of our algorithm in identifying person-speci c features Another factor that limits the performance of our algorithm is illumination variations in the captured images Especially problematic are variations between outdoor indoor and day night settings.

#include <algorithm> #include <vector> #include <iostreamh> template <class Type> void print_elements( Type elem ) { cout elem " "; } /* * generates: ia sorted into two subarrays: 12 15 17 20 23 26 29 35 40 51 10 16 21 41 44 54 62 65 71 74 ia inplace_merge: 10 12 15 16 17 20 21 23 26 29 35 40 41 44 51 54 62 65 71 74 ivec sorted into two subvectors: 51 40 35 29 26 23 20 17 15 12 74 71 65 62 54 44 41 21 16 10 ivec inplace_merge: 74 71 65 62 54 51 44 41 40 35 29 26 23 21 20 17 16 15 12 10 */ int main() { int ia[] = { 29,23,20,17,15,26,51,12,35,40, 74,16,54,21,44,62,10,41,65,71 }; vector< int, allocator > ivec( ia, ia+20 ); void (*pfi)( int ) = print_elements; // place the two subsequences in sorted order sort( &ia[0], &ia[10] ); sort( &ia[10], &ia[20] ); cout "ia sorted into two sub-arrays: \n"; for_each( ia, ia+20, pfi ); cout "\n\n"; inplace_merge( ia, ia+10, ia+20 ); cout "ia inplace_merge:\n"; for_each( ia, ia+20, pfi ); cout "\n\n"; sort( ivecbegin(), ivecbegin()+10, greater<int>() ); sort( ivecbegin()+10, ivecend(), greater<int>() ); cout "ivec sorted into two sub-vectors: \n"; for_each( ivecbegin(), ivecend(), pfi ); cout "\n\n"; inplace_merge( ivecbegin(), ivecbegin()+10, ivecend(), greater<int>() ); cout "ivec inplace_merge:\n"; for_each( ivecbegin(), ivecend(), pfi ); cout endl; } iter_swap () template <class ForwardIterator1, class ForwardIterator2> void iter_swap ( ForwardIterator1 a, ForwardIterator2 b );

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file:///F|/WinDDK/resources/CPPPrimer/c++primerhtm (1030 / 1065) [2001-3-29 11:32:15]

the authenticator (NAS) to simply peek into the EAP code field and only look for success or failure to allow access to the peer, while passing any other EAP messaging that carry method-specific information without having to understand its content. All the authenticator needs to know is how to pass EAP messages with code 1 (request), 3 or 4 (success or failure) to the peer and EAP messages with code 2 (response) back to the server. This is the core value brought by EAP framework: When new authentication methods are introduced, no software upgrades are required at the network edge devices acting as authenticators, which are typically less sophisticated and large in numbers. Only the peer and the authentication server need to be upgraded to support the new authentication method. The model shown in Figure 10.4 represents the three-party authentication model that is widely spread today. However, there may be rare cases, where the authenticator is capable of performing one or two authentication mechanisms locally and hence the three-party model needs to be simplified to a two-party authentication model. In that scenario, one can use the simpler model shown in Figure 10.5. The model shown also applies to cases where EAP signaling may be used in a peer-to-peer mode, such as some key management applications. The authenticator, by looking at the type field within the EAP packet, should be able to know whether it should participate in a two-party authentication and act on the message or simply forward it to the peer or authentication server as it does in a three-party model.

(Of course, this limitation is not unique to our algorithm) As a strategy to provide additional light unobtrusively under adverse.

iter_swap() swaps the values contained within the elements addressed by the two ForwardIterators a and b

#include <algorithm> #include <list> #include <iostreamh> int main() { int ia[] = { 5, 4, 3, 2, 1, 0 }; list< int,allocator > ilist( ia, ia+6 ); typedef list< int, allocator >::iterator iterator; iterator iter1 = ilistbegin(), iter2, iter_end = ilistend(); // bubble sort the list for ( ; iter1 != iter_end; ++iter1 ) for ( iter2 = iter1; iter2 != iter_end; ++iter2 ) if ( *iter2 < *iter1 ) iter_swap( iter1, iter2 ); // output generated: // ilist after bubble sort using iter_swap(): // { 0 1 2 3 4 5 } cout "ilist afer bubble sort using iter_swap(): { "; for ( iter1 = ilistbegin(); iter1 != iter_end; ++iter1 ) cout *iter1 " "; cout "}\n"; } lexicographical_compare() template <class InputIterator1, class InputIterator2 > bool lexicographical_compare( InputIterator1 first1, InputIterator1 last1, InputIterator2 first2, InputIterator2 last2 ); template < class InputIterator1, class InputIterator2, class Compare > bool lexicographical_compare( InputIterator1 first1, InputIterator1 last1, InputIterator2 first2, InputIterator2 last2, Compare comp );

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