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Adaptive Template Enhancement for Improved Person Recognition using Small Datasets

Yang, Su, Hoque, Sanaul, Deravi, Farzin (2022) Adaptive Template Enhancement for Improved Person Recognition using Small Datasets. arXiv preprint, . (doi:10.48550/arXiv.2201.01218) (KAR id:93715)

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Official URL
https://doi.org/10.48550/arXiv.2201.01218

Abstract

A novel instance-based method for the classification of electroencephalography (EEG) signals is presented and evaluated in this paper. The non-stationary nature of the EEG signals, coupled with the demanding task of pattern recognition with limited training data as well as the potentially noisy signal acquisition conditions, have motivated the work reported in this study. The proposed adaptive template enhancement mechanism transforms the feature-level instances by treating each feature dimension separately, hence resulting in improved class separation and better query-class matching. The proposed new instance-based learning algorithm is compared with a few related algorithms in a number of scenarios. A clinical grade 64-electrode EEG database, as well as a low-quality (high-noise level) EEG database obtained with a low-cost system using a single dry sensor have been used for evaluations in biometric person recognition. The proposed approach demonstrates significantly improved classification accuracy in both identification and verification scenarios. In particular, this new method is seen to provide a good classification performance for noisy EEG data, indicating its potential suitability for a wide range of applications.

Item Type: Article
DOI/Identification number: 10.48550/arXiv.2201.01218
Uncontrolled keywords: Instance-based classification, pattern recognition, biometrics, template reconstruction, time series data.
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Sanaul Hoque
Date Deposited: 24 Mar 2022 12:10 UTC
Last Modified: 25 Mar 2022 09:24 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/93715 (The current URI for this page, for reference purposes)
Hoque, Sanaul: https://orcid.org/0000-0001-8627-3429
Deravi, Farzin: https://orcid.org/0000-0003-0885-437X
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