ECG Biometric Identification Method based on Parallel 2-D Convolutional Neural Networks

ECG Biometric Identification Method based on Parallel 2-D Convolutional Neural Networks

Blog Article

In this paper, an ECG biometric identification method, based on a two-dimensional convolutional neural network, is introduced for biometric applications.The proposed model includes two-dimensional Medical convolutional neural networks that work parallel and receive two different sets of 2-dimensional features as input.First, ACDCT features and cepstral properties are extracted from overlapping ECG signals.

Then, these features are transformed from one-dimensional representation to two-dimensional representation by matrix manipulations.For feature learning purposes, these two-dimensional features are given to the inputs of the proposed model, separately.Finally, score level 3 IN 1 LAVENDER fusion is applied to identify the user.

Our experimental results show that the proposed biometric identification method achieves an accuracy of %88.57 and an identification rate of 90.48% for 42 persons.