Keywords

Transform Domain 2DPCA

Abstract

Recently, pattern recognition/classification has received considerable attention in diverse engineering fields such as biomedical imaging, speaker identification, fingerprint recognition, and face recognition, etc. This study contributes novel techniques for facial image recognition based on the Two dimensional principal component analysis in the transform domain. These algorithms reduce the storage requirements by an order of magnitude and the computational complexity by a factor of 2 while maintaining the excellent recognition accuracy of the recently reported methods. The proposed recognition systems employ different structures, multicriteria and multitransform. In addition, principal component analysis in the transform domain in conjunction with vector quantization is developed which result in further improvement in the recognition accuracy and dimensionality reduction. Experimental results confirm the excellent properties of the proposed algorithms.

Notes

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Graduation Date

2007

Semester

Fall

Advisor

Mikhael, Wasfy

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Electrical Engineering and Computer Science

Degree Program

Electrical Engineering

Format

application/pdf

Identifier

CFE0001977

URL

http://purl.fcla.edu/fcla/etd/CFE0001977

Language

English

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

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