Name
Abstract | ||
|---|---|---|
The recognition of individuals based on behavioral and biological characteristics has made important strides over the past few years. Growing interest has been recently devoted to the study of physiological measures, which include the electrical activity of brain (EEG) and heart (ECG). Even if the use of multimodal approaches overcome several limitations of traditional uni-modal biometric systems, the simultaneous use of EEG and ECG characteristics has been scarcely investigated. In this paper, we present a set of preliminary results derived by the investigation of a biometric system based on the fusion of simple features simultaneously extracted from EEG and ECG signals. The reported results show high performance both from uni-modal approach (higher performance being EER = 11.17 and EER = 3.83 for EEG and ECG respectively) and fusion (EER = 2.94). However, caution should be considered in the interpretation of the reported results mainly beacuse the analysis was performed on a limited set of subjects. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-23222-5_5 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Pattern recognition,Computer science,Artificial intelligence,Biometrics,Simple Features,Biometric system,Electroencephalography | Conference | 9281 |
ISSN | Citations | PageRank |
0302-9743 | 4 | 0.43 |
References | Authors | |
7 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Silvio Barra | 1 | 62 | 11.59 |
| Andrea Casanova | 2 | 40 | 5.58 |
| Matteo Fraschini | 3 | 48 | 8.40 |
| M. Nappi | 4 | 947 | 85.18 |