Figure 18.8. Bidimensional decision space. The ordinates represent the ECG probabilities, and the in .NET

Compose qr barcode in .NET Figure 18.8. Bidimensional decision space. The ordinates represent the ECG probabilities, and the
Figure 18.8. Bidimensional decision space. The ordinates represent the ECG probabilities, and the
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abscissa the EEG probabilities. Red crosses represent impostor cases, and green crosses represent legal cases. Two decision functions are represented.
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18.6 Conclusion Table 18.3. Final Results after Fusion TAR Decision function 1 Decision function 2 97.9% 100 FAR 0.82 0
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on hand. Such a linear decision surface is easy to optimize, because it lives in a low parametrical space. One more decision surface 2 is depicted in Figure 18.8. The relationship between adaptation and generalization capability of a classi er system is very well known. Therefore, 2 is much more adapted to the test data set used in the simulation presented herein. We expect such a decision boundary to present less generalization capability when new subjects enter into the system. However, the performance of 1 is good enough for a practicable biometric system and furthermore, easier to parameterize. From an application point of view, the decision surface 1 will be useful for an application where security issues are not critical (e.g., access to Disneyland, where we are interested that everybody is authenticated even though some intruders get also access to the facilities), while the surface 2 would be used in an application where the security issues are extremely important (e.g., access to radioactive combustible in a nuclear plant, where we really do not want any intruder to get access, even though some legal subject are not allowed to get access). The results in terms of TAR and FAR are shown in Table 18.3.
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CONCLUSION
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We have presented the performance results obtained by a bimodal biometric system based on physiological signals, namely, EEG and ECG. The results demonstrate the validity of the multistage fusion approach taken into account in the system. In this context we undertake fusion at the feature, classi cation, and decision stages, thereby improving the overall performance of the system in terms of acceptance and rejection rates. Moreover, the system presented herein improves the unobtrusiveness of other biometric systems based on physiological signals due to the employment of a wireless acquisition unit (ENOBIO). Moreover, two channels were used for the EEG modality and one channel was used for ECG. It is worth mentioning the implementation of novel EEG features. The inclusion of synchronicity features, which take into account the data of two different channels, complement quite well the usage of one-channel features, which have been traditionally used in biometric systems. On the other hand, those two-channel features are used for the rst time in such a system. The features undergo a LDA classi cation with different discriminant functions. Therefore we take into consideration a set of feature classi ers combinations. This fact improves the robustness of the system and even its performance.
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18 Multimodal Physiological Biometrics Authentication
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After testing the performance of different ECG features, we conclude that the most discriminative one is the heartbeat waveform as a whole. For its extraction, it is necessary to implement a preprocessing stage. The unique feature undergoes a classi cation stage similar to the one used with the modality described above. Therefore different discriminant functions of a LDA classi er present different performance for each of the subjects. The inclusion of their combination results in an improvement in the performance of the overall system. We have demonstrated as well the suitability of including a decision fusion stage, whereby the decision between legal and impostor subjects becomes linear. Moreover, the decision fusion allows to decrease the FPR of the system, which constitutes an important feature of a reliable system. Although the corresponding decision boundary was computed from test results, its parameterization is easily attainable. Optimization procedures can be applied to ful ll this aim. Regarding the security issues, we wish to explain that our system was developed within a European project called HUMABIO (see acknowledgment section and reference 1), in which several biometric modalities are combined to provide a highly reliable decision. All the different modalities are controlled through a central application that interfaces the different sensors with the database. A lot of security aspects have been taken into account and have been implemented in the nal system (cryptography, transaction getaway, digital certi cates, etc.). The details are beyond the scope of the present chapter. On the other hand, since ENOBIO has a wireless component, some additional security aspects should be taken into account during the data transmission, like data encryption. This is one development that will be implemented in the future. We also wish to mention other possible future applications of our system. Using the ENOBIO sensor, which is unobtrusive and wearable, and through the analysis of EEG and ECG signals, we can authenticate other things in addition to the subjects. There is evidence that both EEG and ECG signals can be used to validate the initial state of the subject that is, to detect if the subject is in normal condition and has not taken alcohol or drugs or is not suffering from sleep deprivation [28 30]. Moreover, a continuous authentication system and a continuous monitoring system could also be implemented since the sensor, as already explained, is unobtrusive and wearable. A further step is to extract emotions from ECG and EEG [31,32]. This would be very useful for human computer interactions. As an example, we can think on virtual reality applications where the reactions of the computer generated avatars would take into account the emotions of the subject immersed in the virtual reality environment [33].
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