Biometric identity recognition based on contrastive positive-unlabeled learning

Abstract

With the development of neural networks, identity recognition techniques utilizing human physiological signals have found applications in healthcare systems. However, many current physiological signal datasets have to spend a lot of resources in the pre-labeling phase, which leads to longer training times. To reduce the cost of labeling, we propose a self-supervised time-series representation learning framework based on contrastive positive-unlabeled learning (TS − CPUL), to learn the representations of temporal neighborhood sequences. To obtain the representations, the unit root test divides the unlabeled time series into neighborhood samples and unlabeled samples. Secondly, we use neighborhood sample representations and unlabeled sample representations to do self-supervised contrastive learning and positive-unlabeled learning with a negative sample selector. Both approaches can deepen the model’s understanding of the sample representation. Lastly, to enhance the model’s capability in discriminating between ambiguous sample pairs, we design the distance-polarized matrix. The proposed TS-CPUL is evaluated through experiments on three physiological signal datasets. The results demonstrate its effectiveness in understanding time-series representations and performing well in classification. For the identity recognition task, we achieve accuracy comparable to the supervised model by fine-tuning the model with only 10% of the labeled data.