Open AccessComparative analysis of bag‐of‐words models for ECG‐based biometrics
Author(s) -
Ciocoiu Iulian B.
Publication year - 2017
Publication title -
iet biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 28
ISSN - 2047-4946
DOI - 10.1049/iet-bmt.2016.0177
Subject(s) - computer science , biometrics , pooling , robustness (evolution) , artificial intelligence , pattern recognition (psychology) , waveform , segmentation , word error rate , speech recognition , telecommunications , biochemistry , chemistry , radar , gene
The performances of the bag‐of‐words approach in biometric applications using electrocardiography (ECG) signals have been analysed according to the influence of specific design parameters. Optimal setup scenarios have been identified combining five encoding procedures, two pooling methods, and three classification strategies. The method does not require waveform segmentation nor fiducial points detection. Comparative results based on extensive experiments conducted on real ECG recordings collected on chest, finger, and hand palm are presented. Sparse representations yield best results, exceeding 99% correct classification rate for a number of 100 subjects, while additionally exhibiting robustness against modifications of the experimental setup.