Biometrics-based user identification with optimal feature evaluation and selection

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dc.contributor.advisorSha, Kewei
dc.contributor.committeeMemberYue, Kwok-Bun
dc.contributor.committeeMemberWei, Wei
dc.creatorKayastha, Namrata
dc.creator.orcid0000-0002-5974-8457
dc.date.accessioned2020-02-05T17:50:12Z
dc.date.available2020-02-05T17:50:12Z
dc.date.created2019-12
dc.date.issued2019-12-16
dc.date.submittedDecember 2019
dc.date.updated2020-02-05T17:50:13Z
dc.description.abstractRecently, biometrics-based identification algorithms have gained popularity as a means of identifying a person using their unique behavioral characteristics such as gait or hand movement pattern. Classifications based on biometric features are broadly used in modern healthcare applications, including user identification, authentication, and tracking. The complexity and accuracy of classification algorithms largely depend on the size and the quality of the feature set used to build classifiers. In this thesis, we mostly focus on feature evaluation and selection as these are the essential steps to decide a small set of high-quality features to build accurate and efficient classifiers in user identification. We propose a novel and efficient approach to evaluate and select biometric features for user identification based on activity sensor data collected from the users’ wrists while they are walking. For each feature, we first generate an NRMSD matrix, each entry of which represents the similarity level of any two users. Based on the observations from NRMSD matrices, we define two heuristics, Farness Value and Farness Ratio to evaluate the quality of the feature. We evaluated a total of 72 features and selected 18 high-quality features based on our evaluation results. Finally, we train our data with different classifiers and select KNN as the best classification model. Compared to other feature evaluation and selection techniques, this approach is more efficient and yields a higher accuracy of 98.3%.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/10657.1/2141
dc.language.isoen
dc.subjectBiometrics
dc.subjectIdentification
dc.subjectClassification
dc.subjectFeature Evaluation
dc.subjectFeature Selection
dc.titleBiometrics-based user identification with optimal feature evaluation and selection
dc.typeThesis
dc.type.materialtext
thesis.degree.grantorUniversity of Houston-Clear Lake
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

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