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In this section, we describe reliability requirements for accounting protocols. Typically, accounting faults include packet loss, accounting server failures, network failures, and server reboots, and it is important that accounting management systems be scalable and reliable. However, different accounting applications may impose different requirements on accounting protocol. In applications such as usage-sensitive billing, cost allocation, and auditing, loss of accounting data can translate to revenue loss and there is an incentive to engineer a high degree of fault resilience. Some of the application-specific reliability requirements are listed below.

Our tracking algorithm provides 3D estimates of motion, illumination model parameters, and identity and deformation parameters, thus going beyond illuminationinvariant 2D tracking [3, 16]. It does not require a texture mapped 3D model of the object as in [17], which can be a severe restriction in many application scenarios, like face recognition. For tracking faces, it is more computationally ef cient than 3DMM approaches [18] since it approximates the pose appearance space as a series of locally linear tangent planes, while 3DMM works by nding the best t on the nonlinear manifold (requiring computationally expensive transformations). There is a small, but not signi cant (for most applications), trade-off in accuracy in the process. Video-Based Face Recognition Using GAMs. The probabilistic IC tracking on the GAMs described above is then used for video-based face recognition. We assume that a 3D model of each face in the gallery is available. For our experiments, the 3D model is estimated from images, but any 3D modeling algorithm, including directly acquiring the model through range sensors, can be used for this purpose. Given a probe sequence, we track the face automatically in the video sequence under arbitrary pose and illumination conditions using the probabilistic IC tracking on the GAMs. This tracking requires only a generic 3D shape model. The learned illumination parameters are used to synthesize video sequences for each gallery under the motion and illumination conditions in the probe. The distance between the probe and synthesized sequences is then computed for each frame. Different distance measurements are explored for this purpose. Next, the synthesized sequence that is at a minimum distance from the probe sequence is computed and is declared to be the identity of the person.

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string sentence( "kind of" ); string s1( "whistle" );

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A broad review of face recognition is available in [8]. Recently, there have been a number of algorithms for pose and/or illumination invariant face recognition, many of which are based on the fact that the image of an object under varying illumination lies in a lower-dimensional linear subspace. In [19], the authors proposed a 3D spherical harmonic basis morphable model (SHBMM) to implement a face recognition system given one single image under arbitrary unknown lighting. Another 3D face morphable model (3DMM)-based face recognition algorithm was proposed in [20]; but they used the Phong illumination model, estimation of whose parameters can be more dif cult in the presence of multiple and extended light sources. A novel method for multilinear independent component analysis was proposed in [4] for pose and illumination invariant face recognition. All of the above methods deal with recognition in a single image or across discrete poses and do not consider continuous video sequences. Video-based face recognition requires integrating the tracking and recognition modules and exploitation of the spatiotemporal coherence in the data. In [7], the authors deal with the issue of videobased face recognition, but concentrate mostly on pose variations. Similarly the

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One sort of nuisance detail of a text query system is the need to recognize the same word differing by tense, such as cry, cries, and cried, by number, such as baby and babies, and, more trivially, by capitalization, such as home and Home The first two cases belong to the problem of word suffixing Although the general problem of suffixing is outside the scope of this text, the following small sample solution provides a good exercise of the string class operations Before we turn to suffixing, however, let's first solve the simpler case of capitalization Rather than try to be smart in recognizing special cases, we'll just replace all capital letters with their lowercase form Our implementation looks like this:

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