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- W2910544057 abstract "The risk of tampering exists for conventional user recognition methods based on biometrics such as face and fingerprint. Recently, research on user recognition using biometric signals such as electrocardiogram (ECG), electroencephalogram (EEG), and electromyogram (EMG) has been actively performed to overcome this issue. We herein propose a user recognition method applying a deep learning technique based on ensemble networks after transforming ECG signals into two-dimensional (2D) images. A preprocessing process for one-dimensional ECG signals is performed to remove noise or distortion; subsequently, they are projected onto a 2D image space and transformed into image data. For the proposed algorithm, we designed deep learning-based ensemble networks to improve the degraded performance arising from overfitting in a single network. Our experimental results demonstrate that the proposed ensemble networks exhibit an accuracy that is 1.7% higher than that of the single network. In particular, the performance of the ensemble networks is up to 13% higher compared to the single network that degrades the recognition rate by displaying similar features between classes." @default.
- W2910544057 created "2019-01-25" @default.
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- W2910544057 date "2019-01-12" @default.
- W2910544057 modified "2023-10-11" @default.
- W2910544057 title "A study on user recognition using 2D ECG based on ensemble of deep convolutional neural networks" @default.
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- W2910544057 doi "https://doi.org/10.1007/s12652-019-01195-4" @default.
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