An enhancement of LDA algorithm for face recognition (Record no. 25424)

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fixed length control field 02916nam a2200313Ia 4500
001 - CONTROL NUMBER
control field 77209
003 - CONTROL NUMBER IDENTIFIER
control field ft3963
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20251106180942.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 190327n 000 0 eng d
040 ## - CATALOGING SOURCE
Description conventions rda
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Language code of text/sound track or separate title engtag
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Classification number QA76 L35 2005
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100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Don Angelo Lalicon and Opalyne Jamoralin.
245 #0 - TITLE STATEMENT
Title An enhancement of LDA algorithm for face recognition
264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture .
Name of producer, publisher, distributor, manufacturer .
Date of production, publication, distribution, manufacture, or copyright notice c2005
300 ## - PHYSICAL DESCRIPTION
Other physical details Undergraduate Thesis: (BSCS major in Computer Science) -Pamantasan ng Lungsod ng Maynila, 2005.
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Content type term text
Source rdacontent
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Media type term unmediated
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Audience term 2
505 ## - FORMATTED CONTENTS NOTE
Formatted contents note ABSTRACT: This research paper LDA (Linear Discriminant Algorithm) for Face Recognition was inspired by the previous thesis “Human Face Detection and Recognition” by Christine Joy Espiritu and Aaron James Singson. In face recognition, many techniques have been proposed. Among them, the most notable is a two-stage PCA+LDA approach Principal Component Analysis (PCA) is used to project images from the original image space into a face-subspace, where dimensionality is reduced and is no longer degenerate, so that LDA can proceed without trouble, PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) are methods commonly used for face recognition and verification PCA was used in Human Face Dtection and Recognition by the previous proponents in developing their paper. However, there is a certain research “An Efficient LDA Algorithm for Face Recognition” by Jie yang, Hua Yu and William Kunz that proves that LDA algorithms outperformed the PCA. On the other hand, there are certain issues that haunt the LDA algorithm.
520 ## - SUMMARY, ETC.
Summary, etc. ABSTRACT: This research paper LDA (Linear Discriminant Algorithm) for Face Recognition was inspired by the previous thesis Human Face Detection and Recognition by Christine Joy Espiritu and Aaron James Singson. In face recognition, many techniques have been proposed. Among them, the most notable is a two-stage PCA+LDA approach Principal Component Analysis (PCA) is used to project images from the original image space into a face-subspace, where dimensionality is reduced and is no longer degenerate, so that LDA can proceed without trouble, PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) are methods commonly used for face recognition and verification PCA was used in Human Face Dtection and Recognition by the previous proponents in developing their paper. However, there is a certain research An Efficient LDA Algorithm for Face Recognition by Jie yang, Hua Yu and William Kunz that proves that LDA algorithms outperformed the PCA. On the other hand, there are certain issues that haunt the LDA algorithm.
526 ## - STUDY PROGRAM INFORMATION NOTE
Classification Filipiniana
540 ## - TERMS GOVERNING USE AND REPRODUCTION NOTE
Terms governing use and reproduction 5
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Genre/form data or focus term academic writing
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Institution code [OBSOLETE] lcc
Item type Thesis/Dissertation
Source of classification or shelving scheme
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Permanent Location Current Location Shelving location Fund Source Total Checkouts Full call number Barcode Date last seen Item type
          Filipiniana-Thesis PLM PLM Archives Donation   QA76 L35 2005 FT3963 2025-09-20 Thesis/Dissertation

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