Multi-tasks Biometric System for Personal Identification

Recently, biometrics systems based on electroencephalogram (EEG) have received growing interest in identifying persons due to the EEG interesting characteristics. Several studies indicated that EEG is a robust biometric that can greatly improve identification rates. Biometric methods depending on multiple tasks are favorable than those based on a single task. Multi-tasks approaches can enhance personal recognition rates and also reduce the possibility of falsifying biometric data. Furthermore, biometric systems relying on cognitive tasks are harder to be reproduced. Therefore, this paper proposes a new EEG- biometric system based on multiple cognitive tasks. The method examines the placements of the electrodes on different sites on the scalp and selects a group of sites that influence the accuracy of the system. The results show that the proposed biometric system is capable of accurately recognizing persons. Also, the electrodes placed on frontal, temporal, and parietal sites are adequate to correctly identify persons with an identification accuracy of 100%. It has a challenging performance compared to the state of the art techniques. Thus, the proposed technique is robust and can be adopted in areas which require high security and confidentiality.