Algorithms to Process and Measure Biometric Information Content in Low Quality Face and Iris Images

Algorithms to Process and Measure Biometric Information Content in Low Quality Face and Iris Images

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dc.contributor.author Youmaran, Richard
dc.date.accessioned 2011-02-02T19:45:39Z
dc.date.available 2011-02-02T19:45:39Z
dc.date.created 2011 en_US
dc.date.issued 2011-02-02
dc.identifier.uri http://hdl.handle.net/10393/19729
dc.description.abstract Biometric systems allow identification of human persons based on physiological or behavioral characteristics, such as voice, handprint, iris or facial characteristics. The use of face and iris recognition as a way to authenticate user’s identities has been a topic of research for years. Present iris recognition systems require that subjects stand close (<2m) to the imaging camera and look for a period of about three seconds until the data are captured. This cooperative behavior is required in order to capture quality images for accurate recognition. This will eventually restrict the amount of practical applications where iris recognition can be applied, especially in an uncontrolled environment where subjects are not expected to cooperate such as criminals and terrorists, for example. For this reason, this thesis develops a collection of methods to deal with low quality face and iris images and that can be applied for face and iris recognition in a non-cooperative environment. This thesis makes the following main contributions: I. For eye and face tracking in low quality images, a new robust method is developed. The proposed system consists of three parts: face localization, eye detection and eye tracking. This is accomplished using traditional image-based passive techniques such as shape information of the eye and active based methods which exploit the spectral properties of the pupil under IR illumination. The developed method is also tested on underexposed images where the subject shows large head movements. II. For iris recognition, a new technique is developed for accurate iris segmentation in low quality images where a major portion of the iris is occluded. Most existing methods perform generally quite well but tend to overestimate the occluded regions, and thus lose iris information that could be used for identification. This information loss is potentially important in the covert surveillance applications we consider in this thesis. Once the iris region is properly segmented using the developed method, the biometric feature information is calculated for the iris region using the relative entropy technique. Iris biometric feature information is calculated using two different feature decomposition algorithms based on Principal Component Analysis (PCA) and Independent Component Analysis (ICA). III. For face recognition, a new approach is developed to measure biometric feature information and the changes in biometric sample quality resulting from image degradations. A definition of biometric feature information is introduced and an algorithm to measure it proposed, based on a set of population and individual biometric features, as measured by a biometric algorithm under test. Examples of its application were shown for two different face recognition algorithms based on PCA (Eigenface) and Fisher Linear Discriminant (FLD) feature decompositions. en_US
dc.language.iso en en_US
dc.subject biometrics en_US
dc.subject image processing en_US
dc.subject face recognition en_US
dc.subject iris recognition en_US
dc.subject image enhancement en_US
dc.subject image segmentation en_US
dc.subject biometric information en_US
dc.subject feature extraction en_US
dc.subject biometric image quality assessment en_US
dc.subject low quality images en_US
dc.title Algorithms to Process and Measure Biometric Information Content in Low Quality Face and Iris Images en_US
dc.type thesis en_US
dc.faculty.department Ottawa-Carleton Institute for Electrical and Computer Engineering en_US
dc.contributor.supervisor Adler, Andy
dc.embargo.terms immediate en_US
dc.degree.name phd en_US
dc.degree.level doctorate en_US
dc.degree.discipline engineering en_US

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