Journal
INFORMATION FUSION
Volume 71, Issue -, Pages 17-27Publisher
ELSEVIER
DOI: 10.1016/j.inffus.2021.01.004
Keywords
User verification; Multimodal; EEG; Siamese Neural Network; LSTM; CNN
Funding
- VC Research [VCR 0000050]
- New Brunswick Innovation Foundation, Canada
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The proposed multi-modal Siamese Neural Network (mSNN) combines EEG signals and offline signatures for improved user verification accuracy. The model achieved a classification accuracy of 98.57% on a dataset of 70 users, outperforming the current state-of-the-art by 12.86%.
Signatures have long been considered to be one of the most accepted and practical means of user verification, despite being vulnerable to skilled forgers. In contrast, EEG signals have more recently been shown to be more difficult to replicate, and to provide better biometric information in response to known a stimulus. In this paper, we propose combining these two biometric traits using a multimodal Siamese Neural Network (mSNN) for improved user verification. The proposed mSNN network learns discriminative temporal and spatial features from the EEG signals using an EEG encoder and from the offline signatures using an image encoder. Features of the two encoders are fused into a common feature space for further processing. A Siamese network then employs a distance metric based on the similarity and dissimilarity of the input features to produce the verification results. The proposed model is evaluated on a dataset of 70 users, comprised of 1400 unique samples. The novel mSNN model achieves a 98.57% classification accuracy with a 99.29% True Positive Rate (TPR) and False Acceptance Rate (FAR) of 2.14%, outperforming the current state-of-the-art by 12.86% (in absolute terms). This proposed network architecture may also be applicable to the fusion of other neurological data sources to build robust biometric verification or diagnostic systems with limited data size.
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