Multi-scale differential feature for ECG biometrics with collective matrix factorization

作者：

Highlights：

• A novel Multi-Scale Differential Feature for ECG Biometrics with Collective Matrix Factorization is proposed.

• The micro texture and multi-scale differential signal characteristics of ECG is efficiently captured.

• The intra-subject and inter-subject similarities are maximally preserved.

• The extracted discriminative ECG representation is more descriptive and robust towards noise.

摘要

•A novel Multi-Scale Differential Feature for ECG Biometrics with Collective Matrix Factorization is proposed.•The micro texture and multi-scale differential signal characteristics of ECG is efficiently captured.•The intra-subject and inter-subject similarities are maximally preserved.•The extracted discriminative ECG representation is more descriptive and robust towards noise.

论文关键词：ECG biometrics,Multi-scale differential feature,Collective matrix factorization,Feature learning

论文评审过程：Received 27 August 2019, Revised 20 November 2019, Accepted 18 January 2020, Available online 20 January 2020, Version of Record 31 January 2020.

论文官网地址：https://doi.org/10.1016/j.patcog.2020.107211