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As mentioned earlier, this model can be deployed for two scenarios: 1. Two co-existing operators providing complementary connectivity service to the user across their access networks. 2. A service provider using an access network provider to reach the user. In the first case, the agreement is a type of roaming agreement, meaning that the user can use both networks but have a business relationship with only one network (the home operator). The user typically also has an initial and permanent security relationship with only the home operator, but establishes a temporary trust relationship with the new operator. This new trust relationship is transitive, i.e. it is derived from the long-term relationship between the two providers and the relationship between the user and her home operator, when needed. By trust relationship, we typically mean that mutual authentication has taken place (the identities of both sides are confirmed) and a proper set of security mechanisms and related keys have been established to protect the communication channels. The user does not have any business relationship with the visited network. This is shown in Figure 11.1. In the second case, where a service provider needs to co-exist with an access provider to reach the user through the access network, the user typically has a separate relationship with both the access network provider and the service provider. We will not consider that case here since the billing model is less sophisticated in that case (the poor user simply

rewrite each of the following using a named cast notation:

7

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(a) (b) (c) (d)

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we see that the recognition performance with the video-based approach is consistently higher than the image-based one, both in Rank 1 performance and in the area under the CMC curve. This trend is magni ed as the average facial pose becomes more nonfrontal. Also, we expect that registration errors, in general, will affect imagebased methods more than video-based methods (since robust tracking maybe able to overcome some of the registration errors, as shown in Section 7.3). It is interesting to compare these results against the results in [19], for imagebased recognition. The size of the databases in both cases is close (though ours is slightly smaller). Our recognition rate with a video sequence at average 15 facial pose (with a range of 15 about the average) is 100%, while the average recognition rate for approximately 20 (called side view) in [19] is 92.4%. For Experiments B and C, [19] does not have comparable cases and goes directly to pro le pose (90 ), which we don t have. Our recognition rate at 45 average pose is 93%. In [19] the quoted rates at 20 is 92% and at 90 is 55%. Thus the trend of our video-based recognition results are signi cantly higher than image-based approaches that deal with both pose and illumination variations.

(void*)ps; = int( *pc ); &dval; (char*) pv;

At the end of our discussion of the increment and decrement operators, we introduced the abstraction of a stack to illustrate the use of the postfix and prefix forms of these operators We conclude this chapter with a brief walk-through of the design and implementation of an iStack class that is, a Stack that supports only elements of type int A stack is a fundamental data abstraction of computer science allowing for the nesting and retrieval of values in LIFO sequence that is, last in, first out The two fundamental operations on a stack are push a new value onto the stack and pop (retrieve) the last value pushed onto the stack Other operations support user queries as to whether the stack is full() or empty() and to determine the size() of that stack that is, how many elements it contains Our initial implementation supports only elements of type int Here is the declaration of its public interface:

#include <vector> class iStack { public: iStack( int capacity ) : _stack( capacity ), _top( 0 ) {} bool pop( int &value ); bool push( int value ); bool full(); bool empty(); void display(); int size(); private: int _top; vector< int > _stack; };

In this chapter, we showed how to combine geometrical and statistical models for video-based face recognition. We showed that it is possible to estimate lowdimensional manifolds that describe object appearance with a small number of training samples using a combination of analytically derived geometrical models and statistical data analysis. We derived a quadrilinear space of object appearance that is able to represent the effects of illumination, motion, identity, and deformation, and we called it the geometry-integrated appearance manifold. Based upon the GAM, we have proposed a method for video-based face recognition. We also collected a face video database consisting of 57 people with large and arbitrary variation in pose and illumination, and we demonstrated the effectiveness of the method on this new database. We showed speci c examples on how to construct this manifold, analyzed the accuracy of the pose and lighting estimates, and presented the video-based face recognition results upon our own data set. Detailed analysis of recognition performance are also carried out. Future work will focus on extending GAMs to objects with large deformations and its application in video-based face recognition with large expression variations.

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