A study of how outlier detectors can accurately authenticate multiple persons using the heart rate from consumer-grade wearables

A study of how outlier detectors can accurately authenticate multiple persons using the heart rate from consumer-grade wearables

Chiriţă, Matei (TU Delft Electrical Engineering, Mathematics and Computer Science)

Tax, D.M.J. (mentor)
Naseri Jahfari, A. (mentor)
Ghorbani, R. (mentor)
Lan, G. (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2023-06-30

The aim of this paper is to complete the gap in the knowledge and experiment using as little as only the heart rate of some subjects to manage to successfully authorise them in some supposed system. The focus will be on the Gaussian Mixture model and the One Class Support Vector Machine, both outlier detectors, because most of the past research was focused on supervised models. Using these two, this paper will experiment with recognising intruders with models trained to distinguish one authorised person and multiple authorised persons. In the first case, multiple data processing methods and hyperparameters will be tested together and compared. In the second case, the goal will be to use and modify the best-found parameters of the first case to train models that are able to detect multiple persons as authorised. This time, there are two methods that this paper is going to look into and compare their performance: training one single model to detect all the subjects and training multiple models, one per subject. The most notable results are with one authorised person, with a score of 0.936, with two authorised, 0.88, and with 12 authorised, 0.713, when using the area under the curve metric.

Outlier detectors
Authorisation
Gaussian mixture model
One Class SVM
Heart rate
Time series
Wearables

http://resolver.tudelft.nl/uuid:b46e2e65-ecfc-4039-bfe4-6c3acf31a998

Student theses

bachelor thesis

© 2023 Matei Chiriţă