Name
Abstract | ||
|---|---|---|
In this work we utilize the inter-subject differences in the electroencephalographic (EEG) signals evoked by visual stimuli for person identification. The identification procedure is divided into classification and verification phases. During the classification phase, we extract the representative information from the EEG signals of each subject and construct a many-to-one classifier. The best-matching candidate is further confirmed in the verification phase by using a binary classifier specialized to the targeted candidate. According to our experiments in which 18 subjects were recruited, the proposed method can achieve 96.4% accuracy of person identification. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-24955-6_81 | ICONIP (1) |
Keywords | Field | DocType |
binary classifier,classification phase,verification phase,person identification,many-to-one classifier,eeg signal,identification procedure,inter-subject difference,visual stimulus,best-matching candidate,targeted candidate,eeg | Binary classification,Pattern recognition,Computer science,Speech recognition,Artificial intelligence,Classifier (linguistics),Visual perception,Electroencephalography | Conference |
Volume | ISSN | Citations |
7062 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jia-Ping Lin | 1 | 0 | 0.34 |
| Yong-Sheng Chen | 2 | 314 | 30.12 |
| Li-Fen Chen | 3 | 39 | 7.16 |