Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.592681
Title: Biometric verification using gait
Author: Sigurnjak, S. K.
Awarding Body: Manchester Metropolitan University
Current Institution: Manchester Metropolitan University
Date of Award: 2013
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Abstract:
The work presented within this document details the development of a novel gait verification system suitable for a variety of applications such as human motion studies, medical analysis and security situations. The human gait is a spatio temporal process involving the coordination and interaction between the nervous, skeletal and muscular systems. Due to inherent variations in the limb lengths, muscle strengths and body mass gait is inherently individual. To develop a suitable feature extraction process a virtual gait laboratory was developed. The virtual laboratory contains virtual character templates articulated with a 32 bone skeleton system using motion capture data. Data was extracted from the character as a series of X, Y and Z translations for pro cessing. The virtual laboratory allows the testing of data extraction processes without the need for direct testing on human subjects. Feature extraction was performed using Principal Component Analysis (PCA). PCA allows data to be compressed and describes as a series of principal scores (PC) containing the weightings of the data. Feature extraction was performed on human subjects with the motions applied to a skeletal system containing individual physical dimensions. A second set of features was created by applying the motions to a single skeletal system. This removed the interpersonal variations from the dataset to explore the difference in classification when these variables have been removed. Overall generic motions are present within the first PC score. Higher PC scores contain unique motion characteristics suitable for classification of the subject s within a database. To verify a subject within the database Linear Discriminant Analysis (LOA) was performed. LOA projects data as a linear combination of features using a t raining data set of known outcomes. A subsequent sample can then be projected into the linear space for classification and verification of the subject within the database.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.592681  DOI: Not available
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