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UBC Theses and Dissertations
Face detection by facets : combined bottom-up and top-down search using compound templates Holst, Glendon Randal
Abstract
As detection domains increase in size and complexity, new techniques are needed to effectively search the image and feature space. In this thesis, I explore one such approach to object recognition in the domain of face detection. This approach, dubbed compound templates, is compared to a single template approach. The developed system, Facets, provides an implementation of both techniques to enable fair comparison. The compound template technique uses subfeatures and spatial models to represent a compound object (such as a face). From these compound models, hypothesis-based search then combines top-down and bottom-up search processes to localize the search within the image and feature space. Detected subfeatures become evidence for facial hypotheses, which then guide local searches for the remaining subfeatures based upon the expected facial configuration. The compound technique is described and a comparison of the compound templates technique with a single template technique in a mug-shot style face domain is presented. A description of the implementation, along with issues surrounding the compound templates approach is also provided. Attention is paid to performance, including both efficiency and accuracy. The results are complex; but the strengths, weaknesses, and various trade-offs of the two techniques are detailed. The combined bottom-up and top-down approach of compound templates demonstrates a clear advantage over bottom-up only approaches. The compound templates approach also demonstrates better performance for feature sparse images, detection accuracy, domain coverage, and for domains with increasing size.
Item Metadata
Title |
Face detection by facets : combined bottom-up and top-down search using compound templates
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Creator | |
Publisher |
University of British Columbia
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Date Issued |
2000
|
Description |
As detection domains increase in size and complexity, new techniques are
needed to effectively search the image and feature space. In this thesis, I
explore one such approach to object recognition in the domain of face
detection. This approach, dubbed compound templates, is compared to a
single template approach. The developed system, Facets, provides an
implementation of both techniques to enable fair comparison.
The compound template technique uses subfeatures and spatial models to
represent a compound object (such as a face). From these compound
models, hypothesis-based search then combines top-down and bottom-up
search processes to localize the search within the image and feature
space. Detected subfeatures become evidence for facial hypotheses,
which then guide local searches for the remaining subfeatures based upon
the expected facial configuration.
The compound technique is described and a comparison of the compound
templates technique with a single template technique in a mug-shot style
face domain is presented. A description of the implementation, along
with issues surrounding the compound templates approach is also
provided. Attention is paid to performance, including both efficiency and
accuracy. The results are complex; but the strengths, weaknesses, and
various trade-offs of the two techniques are detailed.
The combined bottom-up and top-down approach of compound templates
demonstrates a clear advantage over bottom-up only approaches. The
compound templates approach also demonstrates better performance for
feature sparse images, detection accuracy, domain coverage, and for
domains with increasing size.
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Extent |
3997522 bytes
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Genre | |
Type | |
File Format |
application/pdf
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Language |
eng
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Date Available |
2009-07-10
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Provider |
Vancouver : University of British Columbia Library
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Rights |
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.
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DOI |
10.14288/1.0051147
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URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
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Graduation Date |
2000-11
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Campus | |
Scholarly Level |
Graduate
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Aggregated Source Repository |
DSpace
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Item Media
Item Citations and Data
Rights
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.