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Facial strain maps as a biometric source

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Material Information

Title:
Facial strain maps as a biometric source
Physical Description:
Book
Language:
English
Creator:
Kundu, Sangeeta J
Publisher:
University of South Florida
Place of Publication:
Tampa, Fla.
Publication Date:

Subjects

Subjects / Keywords:
Face recognition
Non rigid motion
Material properties reconstruction
Strain
PCA
Dissertations, Academic -- Computer Science -- Masters -- USF   ( lcsh )
Genre:
government publication (state, provincial, terriorial, dependent)   ( marcgt )
bibliography   ( marcgt )
theses   ( marcgt )
non-fiction   ( marcgt )

Notes

Summary:
ABSTRACT: Current two dimensional face recognition methods rely on visible photometric or geometric attributes that are present in the intensity image. In many of these approaches a technique called Principal Component Analysis (PCA) is extensively used. PCA extracts the maximum intensity variations from the set of input images in the form of "eigen" faces which are used as a feature vector. In these approaches the intensity images used were mostly that of the subject's frontal face, which yielded promising results after doing PCA. These approaches however fail in the presence of facial expression, unstable lighting conditions and artifacts such as make-up, glasses etc. Thus, it is desirable to establish a new biometric source that will be least affected bythe afore mentioned factors.This study describes a face recognition method that is designed based on the consideration of anatomical and biomechanical characteristics of facial tissues. During facial expressions such as smile, frown, anger etc, various muscles get activated in tandem. A strain pattern inferred from a face expression can reveal an individual's signature associated with the underlying anatomical structure, and thus has the potential for face recognition. In this study, the strain is computed by measuring the displacement of a point on the face that results from a facial expression such as opening the mouth.The information provided by the change in the depth value for the face across the open and close mouth frames does not provide any information required for computing the strain maps, because the strain map depends on the relative displacements of two points on the face, which remains same with rigid motions of the face such as rotation and translation.Hence the information in the 2D spaceis sufficient to compute strain since the depth is assumed constant. The approach used to calculate strain computes the strain distribution directly using the mathematical definition of strain as the derivative of displacement in 2D space (XY plane). The strain values obtained are converted to gray scale intensity images, which are used as inputs for the intensity based PCA analysis. Experiments were conducted using 62 subjects. The data set comprised of two pairs of images for a subject: closed mouth and open mouth under bright and low light.
Thesis:
Thesis (M.S.C.S.)--University of South Florida, 2005.
Bibliography:
Includes bibliographical references.
System Details:
System requirements: World Wide Web browser and PDF reader.
System Details:
Mode of access: World Wide Web.
Statement of Responsibility:
by Sangeeta J. Kundu.
General Note:
Title from PDF of title page.
General Note:
Document formatted into pages; contains 74 pages.

Record Information

Source Institution:
University of South Florida Library
Holding Location:
University of South Florida
Rights Management:
All applicable rights reserved by the source institution and holding location.
Resource Identifier:
aleph - 001670405
oclc - 62367107
usfldc doi - E14-SFE0001280
usfldc handle - e14.1280
System ID:
SFS0025601:00001


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PAGE 56

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PAGE 63

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PAGE 64

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PAGE 65

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QA76 (Online)
1 100
Kundu, Sangeeta J.
0 245
Facial strain maps as a biometric source
h [electronic resource] /
by Sangeeta J. Kundu.
260
[Tampa, Fla.] :
b University of South Florida,
2005.
502
Thesis (M.S.C.S.)--University of South Florida, 2005.
504
Includes bibliographical references.
516
Text (Electronic thesis) in PDF format.
538
System requirements: World Wide Web browser and PDF reader.
Mode of access: World Wide Web.
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Title from PDF of title page.
Document formatted into pages; contains 74 pages.
520
ABSTRACT: Current two dimensional face recognition methods rely on visible photometric or geometric attributes that are present in the intensity image. In many of these approaches a technique called Principal Component Analysis (PCA) is extensively used. PCA extracts the maximum intensity variations from the set of input images in the form of "eigen" faces which are used as a feature vector. In these approaches the intensity images used were mostly that of the subject's frontal face, which yielded promising results after doing PCA. These approaches however fail in the presence of facial expression, unstable lighting conditions and artifacts such as make-up, glasses etc. Thus, it is desirable to establish a new biometric source that will be least affected bythe afore mentioned factors.This study describes a face recognition method that is designed based on the consideration of anatomical and biomechanical characteristics of facial tissues. During facial expressions such as smile, frown, anger etc, various muscles get activated in tandem. A strain pattern inferred from a face expression can reveal an individual's signature associated with the underlying anatomical structure, and thus has the potential for face recognition. In this study, the strain is computed by measuring the displacement of a point on the face that results from a facial expression such as opening the mouth.The information provided by the change in the depth value for the face across the open and close mouth frames does not provide any information required for computing the strain maps, because the strain map depends on the relative displacements of two points on the face, which remains same with rigid motions of the face such as rotation and translation.Hence the information in the 2D spaceis sufficient to compute strain since the depth is assumed constant. The approach used to calculate strain computes the strain distribution directly using the mathematical definition of strain as the derivative of displacement in 2D space (XY plane). The strain values obtained are converted to gray scale intensity images, which are used as inputs for the intensity based PCA analysis. Experiments were conducted using 62 subjects. The data set comprised of two pairs of images for a subject: closed mouth and open mouth under bright and low light.
590
Adviser: Dr. Dmitry B. Goldgof.
Co-adviser: Dr. Sudeep Sarkar.
653
Face recognition.
Non rigid motion.
Material properties reconstruction.
Strain.
PCA.
690
Dissertations, Academic
z USF
x Computer Science
Masters.
773
t USF Electronic Theses and Dissertations.
4 856
u http://digital.lib.usf.edu/?e14.1280