Towards biometric footstep recognition: Dimensionality reduction-inspired approaches to pressure-based gait recognition
| dc.contributor.advisor | Scheme, Erik | |
| dc.contributor.author | Roberts, Alex | |
| dc.date.accessioned | 2024-08-20T13:43:27Z | |
| dc.date.available | 2024-08-20T13:43:27Z | |
| dc.date.issued | 2024-06 | |
| dc.description.abstract | This thesis explores the use of a family of principal component analysis (PCA)-based approaches to extract discriminative features for pressure-based gait recognition. Two datasets are used; 1) an openly available CASIA-D dataset comprised of barefoot samples from 88 subjects to examine the performance of various pre-features, dimensionality reduction, and deep learning-based approaches, and 2) a custom 7-subject UNB-collected dataset of footprints and non-footprints to explore the feasibility of a PCA-based footprint detection system. Even with relatively few training samples per participant, strong performances were found for a variety of PCA-based approaches, especially when combined with additional feature selection approaches. It was found that the deep-learned PCANet+ combined with Minimum Redundancy Maximum Relevance was the best performing combination for a biometric verification system, achieving a 96.21% accuracy. Furthermore, PCA-based approaches inspired by the concept of eigenfaces were found to effectively discriminate incomplete and non-foot images. | |
| dc.description.copyright | © Alex Roberts, 2024 | |
| dc.format.extent | ix, 87 | |
| dc.format.medium | electronic | |
| dc.identifier.uri | https://unbscholar.lib.unb.ca/handle/1882/38076 | |
| dc.language.iso | en | |
| dc.publisher | University of New Brunswick | |
| dc.rights | http://purl.org/coar/access_right/c_abf2 | |
| dc.subject.discipline | Electrical and Computer Engineering | |
| dc.title | Towards biometric footstep recognition: Dimensionality reduction-inspired approaches to pressure-based gait recognition | |
| dc.type | master thesis | |
| oaire.license.condition | other | |
| thesis.degree.discipline | Electrical and Computer Engineering | |
| thesis.degree.grantor | University of New Brunswick | |
| thesis.degree.level | masters | |
| thesis.degree.name | M.Sc.E. |