Behavioural biometrics are unique non-physical human characteristics that can be used to distinguish one person from another. One such characteristic, which belongs to the Gait Biometric, is the footstep Ground Reaction Force (GRF), the temporal signature of the force exerted by the ground back on the foot through the course of a footstep. This is a biometric for which the computational power required for practical applications in a security setting has only recently become available. In spite of this, there are still barriers to deployment in a practical setting, including large research gaps concerning the effect of footwear and stepping speed on footstep GRF-based person recognition. In this thesis we devised an experiment to address these research gaps, while also expanding upon the biometric system research presented in previous GRF recognition studies.
To assess the effect of footwear on recognition performance we proposed the analysis of a dataset containing samples for two different types of running shoes. While, with regards to stepping speed, we set out to demonstrate that normalizing for step duration will mitigate speed variation biases and improve GRF recognition performance; this included the development of two novel machine learning-based temporal normalization techniques: Localized Least Squares Regression (LLSR) and Localized Least Squares Regression with Dynamic Time Warping (LLSRDTW). Moreover, building upon previous research, biometric system analysis was done over four feature extractors, seven normalizers, and five different classifiers, allowing us to indirectly compare the GRF recognition results for biometric system configurations that had never before been directly compared.
The results achieved for the aforementioned experiment were generally in line with our initial assumptions. Comparing biometrics systems trained and tested with the same footwear against those trained and tested with different footwear, we found an average decrease in recognition performance of about 50%. While, performing LLSRDTW step duration normalization on the data led to a 14-15% improvement in recognition performance over its non-normalized equivalent in our two most stable feature spaces. Examining our biometric system configurations we found that a Wavelet Packet Decomposition-based feature extractor produced our best feature space results with an EER average of about 2.6%, while the Linear Discriminant Analysis (LDA) classifier performed best of the classifiers, about 19% better than any of the others. Finally, while not the intended purpose of our research, the work in this thesis was presented such that it may form a foundation upon which future classification problems could be approached in a wide range of alternative domains. / Graduate / 0800 / 0544 / jericmason@gmail.com
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/5718 |
| Date | 05 November 2014 |
| Creators | Mason, James Eric |
| Contributors | Traore, Issa |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Rights | Available to the World Wide Web |
Page generated in 0.0023 seconds