Robust multi-feature collective non-negative matrix factorization for ECG biometrics

作者：

Highlights：

• We propose a robust multi-feature collective non-negative matrix factorization model.

• We learn an unified representation in the semantic space from multiple LBP histograms.

• We integrate label information and multiple norms to enhance the discrimination.

摘要

•We propose a robust multi-feature collective non-negative matrix factorization model.•We learn an unified representation in the semantic space from multiple LBP histograms.•We integrate label information and multiple norms to enhance the discrimination.

论文关键词：ECG biometrics,Collective non-negative matrix factorization,Multiple features,Local binary pattern,Label information

论文评审过程：Received 24 April 2021, Revised 24 August 2021, Accepted 15 October 2021, Available online 16 October 2021, Version of Record 25 October 2021.

论文官网地址：https://doi.org/10.1016/j.patcog.2021.108376