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Type of Document Dissertation Author Boehnen, Christopher Bensing URN etd-07272009-111718 Title Improving 3D Face Recognition Model Generation and Biometrics Degree Doctor of Philosophy Department Computer Science and Engineering Advisory Committee
Advisor Name Title Kevin Bowyer Committee Member Nitesh Chawla Committee Member Patrick Flynn Committee Member Surendar Chandra Committee Member Keywords
- 3D Face Biometrics Computer Vision
Date of Defense 2009-04-21 Availability unrestricted Abstract 3D face shape biometrics, with greater pose and lighting condition data invariancethan 2D (photometric), has the potential to yield superior performance to 2D data for
some applications. However, many of the capture limitations of 3D scanners are the same
as those of 2D biometric capture devices with respect to lighting, environmental, and
subject configurations. Many of the claimed advantages of 3D over 2D do not exist under
current capture configurations. In addition, 3D scanners themselves are more expensive
than 2D cameras, and 3D biometric data are unavailable on many of the subjects we
would like to identify. Further, the significant computational cost of 3D face recognition
has made large scale deployment of 3D face recognition impractical. The focus of this
thesis is to address these issues to improve the feasability of 3D face recognition so that it
is more applicable outside of a research environment. In particular, the focus is on
improving the methods and hardware needed to produce a 3D model of a face, improving
biometric recognition and verification performance, and decreasing the computational
cost to allow for larger scale applications. In this thesis, I propose a new structure from
motion approach, a new fast 3D face biometric, and examine the impact of movement on
existing structure from light devices.
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