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lighting conditions, we are employing infrared LED illuminators in conjunction with an infrared-sensitive camera. In summary, while there have been many different feature-based approaches to face recognition over the last two decades of research, we have proposed a novel methodology based on the discovery and extraction of person-speci c characteristic features to improve face recognition performance for small face databases. This approach is aimed at facilitating social interaction in casual settings. The use of Gabor features, in tandem with a genetic algorithm to discover characteristic person-speci c features, has been inspired by the human visual system and is based on knowledge that has been developed about the process by which humans recognize faces. We believe that more needs to be learned about human face recognition; and we also believe that as more is learned, the knowledge can be put to use to develop more robust face recognition algorithms.

lexicographical_compare() compares the corresponding pair of elements of the two sequences identified by [first1,last1) and [first2,last2) The comparison continues until either the element pair does not match, the pair [last1,last2] is reached, or either last1 or last2 is reached (if the sequences are not of equal size) For the first pair of elements that do not match, the following occurs:

If the element of the first sequence is less, return true; otherwise, return false If last1 is reached, but last2 is not, return true If last2 is reached, but last1 is not, return false

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1. A. Schwaninger, C.-C. Carbon, H. Leder, Expert Face Processing: Specialization and Constraints, In G. Schwarzer & H. Leder, Development of face processing, Goettingen: Hogrefe, pp. 81 97. 2. V. Bruce and A. Young, In the Eye of the Beholder: The Science of Face Perception, Oxford University Press, New York, 2006.

If both last1 and last2 are reached (all the elements match), return false; that is, the first sequence is not lexicographically less than the second sequence

Figure 10.5 EAP layering model for EAP-XXX in a two-party model (when authenticator acts as EAP server or end point)

string arr1[] = { "Piglet", "Pooh", "Tigger" }; string arr2[] = { "Piglet", "Pooch", "Eeyore" };

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the algorithm matches on the first element pair, but does not match on the second Pooh evaluates as greater than Pooch because the c is lexicographically less than h (think of the comparison as the ordering done of words listed within a dictionary) The algorithm stops at this point (the third elements are never compared) The result of the comparison is false The second version of the algorithm takes a predicate comparison object rather than using the less than operator of the underlying element type

#include <algorithm> #include <list> #include <string> #include <asserth> #include <iostreamh> class size_compare { public: bool operator()( const string &a, const string &b ) { return alength() <= blength(); } }; int main() { string arr1[] = { "Piglet", "Pooh", "Tigger" }; string arr2[] = { "Piglet", "Pooch", "Eeyore" }; bool res; // evaluates to false at second element // Pooch is less than Pooh // would also evaluate false at third element res = lexicographical_compare( arr1, arr1+3, arr2, arr2+3 ); assert( res == false ); // evaluates to true: each element of ilist2 // has a length less than or equal to the // associated ilist1 element list< string, allocator > ilist1( arr1, arr1+3 ); list< string, allocator > ilist2( arr2, arr2+3 ); res = lexicographical_compare( ilist1begin(), ilist1end(), ilist2begin(), ilist2end(), size_compare() ); assert( res == true ); cout "ok: lexicographical_compare succeeded!\n"; } lower_bound() template< class ForwardIterator, class Type > ForwardIterator

3 J Sadr, I Jarudi, and P Shinha, The role of eyebrows in face recognition, Perception 32:285 293, 2003 4 A W Young, D Hellawell, and D C Hay, Con gurational information in face perception, Perception 16:747 759, 1987 5 P Sinha, B J Balas, Y Ostrovsky, and R Russell, Face recognition by humans: 19 results all computer vision researchers should know about, Proc IEEE 94(11):1948 62, 2006 6 H R McConachie, Developmental prosopagnosia: A single case report, Cortex 12:76 82, 1976 7 I Kennerknecht, T Gruter, B Welling, S Wentzek, J Horst, S Edwards, and M Gruter, First report of prevalence of non-syndromic hereditary prosopagnosia (hpa), Am J Med Genet Part A 140:1617 1622, 2006 8 D J Turk, T C Handy, and M S Gazzaniga, Can perceptual expertise account for the own-race bias in face recognition A split brain study Cognit Neuropsychol 22(7):877 883, 2005 9 P.

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lower_bound( ForwardIterator first, ForwardIterator last, const Type &value ); template< class ForwardIterator, class Type, class Compare > ForwardIterator lower_bound( ForwardIterator first, ForwardIterator last, const Type &value, Compare comp );

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