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Type of Document Dissertation Author Chang, Kyong Author's Email Address jin.chang@gmail.com URN etd-12162004-110305 Title New Multi-Biometric Approaches for Improved Person Identification Degree Doctor of Philosophy Department Computer Science and Engineering Advisory Committee
Advisor Name Title Kevin Bowyer Committee Co-Chair Patrick Flynn Committee Co-Chair Keywords
- computer vision
- biometrics
- face recognition
- home land security
Date of Defense 2004-12-10 Availability unrestricted Abstract Multiple modality biometric approaches are proposed integrating
two-dimensional face appearance with ear appearance,
three-dimensional face shape, and the pattern of
heat emission on face. A single source biometric recognition
method, such as face, has been shown to improve its identification
rate by incorporating other biometric sources. The investigation of
multi-modal biometrics involves a variety of sensors. For the recognition task, each
sensor captures different aspects of human facial features; for example,
appearance representing the levels of brightness on surface
reflectance by a light source, shape data representing depth values
defined at points on an object, and the pattern of heat emitted
from an object. The results of our multiple biometric approach shown
in this investigation appear to support the conclusion that the path
to higher accuracy and robustness in biometrics involves the use of
multiple biometrics rather than the best possible sensor and
algorithm for a single biometric. A new evaluation scheme
is designed to assess the improvement gained by multiple biometrics.
Because multi-modal recognition employs multiple samples of facial
data, it is also possible that the improvement achieved over considering
multiple samples from all modalities for recognition. Therefore,
this evaluation scheme will determine the recognition accuracy
gained by multiple modality approach and multiple sample approach.
Also, a new algorithm for 3D face recognition is proposed for handling
expression variation. It uses a surface registration-based technique for 3D face
recognition. We evaluate and compare the performance of approaches to 3D
face recognition based on PCA-based and on iterative
closest point algorithms. The proposed 3D face recognition method
is fully automatic to use to initialize the
3D matching. The evaluation results show that the proposed algorithm
substantially improves performance in the case of varying facial
expression. This is the first study to compare
the PCA and ICP approaches to 3D face recognition, and the first to propose
a multiple-region approach to coping with expression variation in 3D face
recognition. The proposed method outperforms 3D eigenfaces when 3D
face scans were acquired in different times without expression
changes and also with expression changes.
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