Shifted 1D-LBP Based ECG Recognition System

Regouid, Meryem; Benouis, Mohamed

doi:10.1007/978-3-030-05481-6_13

Cited by 6 publications

(1 citation statement)

References 17 publications

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“…In this regard, few researchers considered using LBP in ECG recognition either as a stand-alone signal representation technique or combined with other methods. In [42], the LBP descriptor is used to extract ECG fiducial features. The performance is tested using ECG signal for 90 subjects from ECG-ID dataset.…”

Section: The Proposed Approachmentioning

confidence: 99%

An Improved Behavioral Biometric System based on Gait and ECG signals

Guelta¹,

Tlemsani²,

Chouraqui³

et al. 2019

IJIES

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This paper presents multi-modal biometric authentication approach using gait and electrocardiogram (ECG) signals, which can diminish the drawback of unimodal biometric approach as well as to improve authentication system performance. In acquisition phase, data sets are collected from three different databases, ECG-ID, MIT-BIH Arrhythmia database and UCI Machine Learning Repository (Gait). In Feature extraction phase of both signals (ECG and Gait) is performed by using 1D-local binary pattern. Features are obtained by merging two modalities as one feature. In classification approach, three classifiers are developed to classify subjects. K-nearest neighbour (KNN), relying on Euclidean distance, PNN (Probabilistic Neural Network), RBF (Radial Basis Function) and Support Vector Machine (SVM), relying on One-against-all (OAA). The proposed multimodal system has been tested over 18 subjects, and its identification accuracy was about 100%. Our result demonstrate that our approach outperforms rather than unimodal biometric system in terms of Correct Recognition Rate, Equal Error Rate, False Acceptance Rate and False Reject Rate.

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Section: The Proposed Approachmentioning

confidence: 99%