MindID

Abstract

Person identification technology recognizes individuals by exploiting their unique, measurable physiological and behavioral characteristics. However, the state-of-the-art person identification systems have been shown to be vulnerable, e.g., anti-surveillance prosthetic masks can thwart face recognition, contact lenses can trick iris recognition, vocoder can compromise voice identification and fingerprint films can deceive fingerprint sensors. EEG (Electroencephalography)-based identification, which utilizes the user's brainwave signals for identification and offers a more resilient solution, has recently drawn a lot of attention. However, the state-of-the-art systems cannot achieve similar accuracy as the aforementioned methods. We propose MindID, an EEG-based biometric identification approach, with the aim of achieving high accuracy and robust performance. At first, the EEG data patterns are analyzed and the results show that the Delta pattern contains the most distinctive information for user identification. Next, the decomposed Delta signals are fed into an attention-based Encoder-Decoder RNNs (Recurrent Neural Networks) structure which assigns varying attention weights to different EEG channels based on their importance. The discriminative representations learned from the attention-based RNN are used to identify the user through a boosting classifier. The proposed approach is evaluated over 3 datasets (two local and one public). One local dataset (EID-M) is used for performance assessment and the results illustrate that our model achieves an accuracy of 0.982 and significantly outperforms the state-of-the-art and relevant baselines. The second local dataset (EID-S) and a public dataset (EEG-S) are utilized to demonstrate the robustness and adaptability, respectively. The results indicate that the proposed approach has the potential to be widely deployed in practical settings.