Thesis - Instituto de Telecomunicações

6.4. CONCLUSION 135time(s) EDA EER ECG EER fusion EER std10 18.1 4.8 4.8 0.230 12.5 3.4 2.8 0.450 7.5 2.4 2.2 0.170 7.0 2.2 1.3 0.290 6.9 1.7 1.1 0.1Table 6.4: Mean equal error rate for sympathetic dynamics, heart dynamics and their fusionin a sequential classifier mode. EER values are presented in %.we can observe a significant increase of the overall performance of the multimodal system,achieving an EER ∼ 0.011 for 90s of signal acquisition, while the individual authenticationsystem, had performance of 0.015 EER in the ECG and 0.068 EER in the EDA standalonebiometric system.6.4 ConclusionWe have presented experimental results of the three proposed behavioral biometrics. Theresults for pointer dynamics and heart dynamics have shown to be adequate for long acquisitionsor continuous monitoring environments. The sympathetic dynamics trait (EDA)presented additional difficulties that motivated the proposal of a novel uncertainty basedclassification fusion method. The multimodal fusion with soft-biometric data sources canlead to a scenario where the fusion decreases the error probability of the hard-biometricclassifier. Interesting results for the multimodal soft-biometric fusion problem have beenpresented, where our approach guarantees sensitivity to the modeling process uncertainty,creating a method where the fusion will maintain or improve the final classification performance.