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would use the same classi ers. We decided to use ve classi ers since this number showed a good compromise between the performance and the computational time. Authentication Process. Once we have the ve best classi ers for all the N enrolled subjects, we can then implement and test our nal application. We now proceed in a similar way, but we only use 1 min of recording data; that is, we input in each one of the ve best classi ers 15 feature vectors (we remind the reader that the authentication test takes have a duration of 1 min; and these takes, as we did in the enrollment case, are segmented in 4-s epochs). Each classi er outputs a posterior matrix (Table 18.2). In order to fuse the results of the ve classi ers, we vertically concatenate the ve obtained posterior matrices and take the column average. The resulting vector is the one we will use to take the authentication decision. In fact, it is a probability density function (PDF; see Figure 18.6 and 18.7): r The rst element is the probability that the single-minute test data come from subject 1. r The second element is the probability that the single-minute test data come from subject 2. r etc.

function getCompany_Result(companyRecord) { companyName_txt.text = companyRecord.items[0].CompanyName; address_txt.text = companyRecord.items[0].Address; city_txt.text = companyRecord.items[0].City; state_txt.text = companyRecord.items[0].State; zipCode_txt.text = companyRecord.items[0].ZipCode; } function updateCompany() { companyService.updateCompany({ CompanyID:companyToShow_cb.getSelectedItem().data, CompanyName:companyName_txt.text, Address:address_txt.text, City:city_txt.text, State:state_txt.text, ZipCode:zipCode_txt.text }); } function updateCompany_Result() { companyService.ListCompanies(); } function updateCompany_Status() { status_txt.text = An error occurred. ; } companyService.listCompanies(); stop();

These three include statements bring the ActionScript code from these script files into your script, so as you test and publish your Flash movie, the code from these external scripts isn t left behind. Notice that include statements do not end in semicolons.

Figure 18.6. PDF for one of the enrolled subjects. The subject is classi ed against his training data

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allow encoding to proceed along the lines of a multiple-choice exercise in rst establishing a bounding range on verbal expressions by eliminating those which do not apply, then converging on the most appropriate one(s), followed by re nement of an interpolated probability value as needed. The de ned conventions are purposefully truncated at probabilities of 0.01 and 0.99, since cognitive discrimination limitations usually produce little meaning for probabilities outside this range. At the same time, this maintains encoded values within or at least reasonably near the well-calibrated range and thereby provides some structural limitation on the effects of overcon dence bias. These constraints are relaxed in some circumstances, such as when indicated by further decomposition or by applicable base-rate frequency information.

on during Flash Remoting development. More on how to use it in the section NetConnection Debugger. This line of code should be removed before production deployment.

set (class 1) and the training data sets of the reference subjects (from class 2 to class 33). In this example, he/she will be correctly authenticated with a high con dence level.

it is technically not needed for Flash Remoting development per se, most developers we know use its extended methods as standard operating procedure (as do we).

Company combo box that you created in the section Create the Company combo box,

The last step in our algorithm takes into consideration a decision rule over the averaged PDF. We use a threshold applied on the probability of the claimed subject. If the probability of the claimed subject is higher than the applied threshold, then the authentication result is positive. Three values are output by our algorithm: r Binary decision (authentication result) r Score (probability of the claimed subject) r Con dence level (an empiric function that maps the difference between threshold and score to a percentage) In order to evaluate the performance of the system, we proceed as follows. 32 subjects with three 3-min takes are used as reference subjects, and the other eight subjects with four 3-min takes are enrolled in the system as explained in the enrolment process above. For the system testing, we distinguish three cases: when a subject claims to be himself (legal situation) and when a subject claims to be another subject from the database (impostor situation). We have 48 legal situations, 350 impostor situations, and 16 intruder situations. What we do, in order to take all the pro t from our data, is to make all the possible combinations with the authentication takes. Subject 1 will claim to be subject 1 (legal situation), but he will also claim to be all the other enrolled subjects (impostor situation). An intruder will claim to be

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