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Auflistung nach Autor:in "Campisi, Patrizio"

1 - 5 von 5
  • Konferenzbeitrag
    Advanced variants of feature level fusion for finger vein recognition
    (Biosig 2016, 2016) Kauba, Christof; Piciucco, Emanuela; Maiorana, Emanuele; Campisi, Patrizio; Uhl, Andreas
  • Konferenzbeitrag
    EEG based user recognition using BUMP modelling
    (BIOSIG 2013, 2013) Rocca, Daria La; Campisi, Patrizio; Solé-Casals, Jordi
    In this paper the use of electroencephalogram (EEG) as biometric identifier is investigated. The use of EEG within the biometric framework has already been introduced in the recent past although it has not been extensively analyzed. In this contribution we apply the “bump” modelling analysis for the feature extraction stage within an identification framework, in order to reduce the huge amount of data recorded through EEG. For the purpose of this study we rely on the “resting state with eyes closed” protocol. The employed database is composed of 36 healthy subjects whose EEG signals have been acquired in an ad hoc laboratory. Different electrodes configurations pertinent with the employed protocol have been considered. A classifier based on Mahalanobis distance have been tested for the enrollment of the subjects and their identification. An information fusion performed at the score level has shown to improve correct classification performance. The obtained results show that an identification accuracy of 99.69% can be achieved. It represents an high degree of accuracy, given the current state of research on EEG biometrics.
  • Konferenzbeitrag
    EEG biometrics for individual recognition in resting state with closed eyes
    (BIOSIG 2012, 2012) Rocca, Daria la; Campisi, Patrizio; Scarano, Gaetano
    In this paper EEG signals are employed for the purpose of automatic user recognition. Specifically the resting state with closed eyes acquisition protocol has been here used and deeply investigated by varying the employed electrodes configuration both in number and location for optimizing the recognition performance still guaranteeing sufficient user convenience. A database of 45 healthy subjects has been employed in the analysis. Autoregressive stochastic modeling and polynomial regression based classification has been applied to extracted brain rhythms in order to identify the most distinctive contributions of the different subbands in the recognition process. Our analysis has shown that significantly high recognition rates, up to 98.73%, can be achieved when using proper triplets of electrodes, which cannot be achieved by employing couple of electrodes, whereas sets of five electrodes in the central posterior region of the scalp can guarantee very high recognition performance while limiting user convenience.
  • Konferenzbeitrag
    EEG biometrics for user recognition using visually evoked potentials
    (BIOSIG 2015, 2015) Das, Rig; Maiorana, Emanuele; Rocca, Daria La; Campisi, Patrizio
    Electroencephalographic signals (EEG) have been long supposed to contain features characteristic of each individual, yet a substantial interest for exploiting them as a potential biometrics for people recognition has only recently grown. The biggest advantages of EEG-based biometrics lie in its universality and security, while its major concerns are related to the acquisition protocol that can be inconvenient and time consuming. This paper investigates the use of EEG signals, elicited using visual stimuli, for the purpose of biometric recognition, and evaluates the performance obtained considering various frequency bands, different number of visual stimuli, and various subsets of time intervals after the stimuli presentation. An exhaustive set of experimental tests has been performed by employing EEG data of 50 different healthy subjects acquired in two different sessions, separated by one week time.
  • Konferenzbeitrag
    Influence of Test Protocols on Biometric Recognition Performance Estimation
    (BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group, 2021) Eglitis, Teodors; Maiorana, Emanuele; Campisi, Patrizio
    The performance of a biometric system is commonly evaluated by the obtained recognition rates and comparing the results against the ones reported in the literature on the same database. An aspect that has not received the deserved attention in the literature concerns the influence, on the achieved rates, of the test protocol employed to select the enrol and probe data. We provide a detailed analysis of the impact of the experimental choices on the estimated performance, considering the recommendations provided by ISO/IEC 19795 standard. We use the UTFVP finger vein database, reproducing results presented in the literature using multiple protocols. Our experiments highlight the possibility of obtaining equal error rates reduced by half simply by changing the test protocol.