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authors in [21] used adaptive hidden Markov models for pose-varying video-based face recognition. A probabilistic framework that fuses the temporal information in a probe video by investigating the propagation of the posterior distribution of the motion and identity was proposed in [22]. Another work used an adaptive appearance model, an adaptive motion model, and an adaptive particle lter for simultaneously tracking and recognizing people in the video [23]. Fisher et al. [24] proposed to perform face recognition by computing the Kullback Leibler divergence between testing image sets and a learned manifold density. Another work [25] used learned manifolds of face variations for face recognition in video. A method for video-based face veri cation using correlation lters was proposed in [26], but the pose in the gallery and probe have to be similar.

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void strip_caps( vector<string,allocator> *words ) { vector<string,allocator>::iterator iter = words->begin(); vector<string,allocator>::iterator iter_end = words->end(); string caps( "ABCDEFGHIJKLMNOPQRSTUVWXYZ" ); while ( iter != iter_end ) { string::size_type pos = 0; while (( pos = (*iter)find_first_of( caps, pos )) != string::npos ) (*iter)[ pos ] = tolower( (*iter)[pos] ); ++iter; } }

tolower( (*iter)[pos] );

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Billing: When accounting data are used for billing purposes, the requirements depend on whether the billing process is usage-sensitive or not. Non-usage-sensitive billing does not require usage information; in theory, all accounting data can be lost without affecting the billing process. This would, however, affect other tasks such as trend analysis, auditing, and data on wholesale information. Usage-sensitive billing processes depend on usage information; therefore, packet loss may translate directly to revenue loss. As a result, the billing process may need to conform to financial reporting and legal requirements, and therefore an archival accounting approach may be needed.

The rest of the chapter is organized as follows. Section 7.2 presents the GAM-based object representation using the analytically derived illumination and motion basis and machine learned basis of identity and deformation. Robust and ef cient tracking algorithms using this object representation are presented in Section 7.3. Then, we propose an integrated tracking and recognition framework for video-based face recognition in Section 7.4. Experimental results and analysis are presented in Section 7.5. Section 7.6 concludes the chapter and highlights future work.

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is a Standard C library function that takes an uppercase character and returns its lowercase equivalent To use it, we must include the header file

(This file includes declarations of other functions such as isalpha(), isdigit(), ispunct(), isspace(), toupper(), and others To find a full listing and discussion, see [PLAUGER92] The Standard C++ library defines a ctype class

file:///F|/WinDDK/resources/CPPPrimer/c++primerhtm (274 / 1065) [2001-3-29 11:32:05]

We will start with the illumination representation of [11] and combine it with motion and shape using the results in [13] in order to derive an analytical representation of a low-dimensional manifold of object appearance with variations in pose and lighting. We will then apply N-mode SVD, a multilinear generalization of SVD, to learn the variation of this manifold due to changes of identity and object deformations. We will show that the image appearance due to variations of illumination, pose, and deformation is quadrilinear, and we will compute the basis functions of this space.

that encapsulates the Standard C library functionality as well as a set of nonmember functions such as toupper(), tolower(), and so on To use them, we must include the Standard C++ header file

As of this writing, however, an implementation of this support is not available to us, so we use the Standard C implementation) Suffixing is difficult to implement rigorously; however, even imperfect implementations yield significant improvements in the quality and size of our collection of words against which to query Our implementation handles only words that end in an 's':

Recently, it was shown that for moving objects it is possible to approximate the sequence of images by a bilinear subspace of nine illumination coef cients and six T motion variables [13]. Representing by T = Tx Ty Tz the translation of the cenT troid of the object, by = x y z the rotation about the centroid, and by Nl (N 9 for Lambertian objects with attached shadow) the illumination coefl R l cients in a spherical harmonics basis (see [11] for details), Xu and Roy-Chowdhury [13] showed that under small motion, the re ectance image at t2 = t1 + t can be

void suffix_text( vector<string,allocator> *words ) { vector<string,allocator>::iterator iter = words->begin(), iter_end = words->end(); while ( iter != iter_end ) { // if 3 or fewer characters, let it be if ( (*iter)size() <= 3 ) { ++iter; continue; } if ( (*iter)[ (*iter)size()-1 ] == 's' ) suffix_s( *iter ); // additional suffix handling goes here such as // ed, ing, ly ++iter; } }

Trend analysis and capacity planning: In trend analysis and capacity planning, the forecasts are inherently imperfect, high reliability is typically not required, and moderate packet loss can be tolerated.

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