An enhancement of LDA algorithm for face recognition

By: Don Angelo Lalicon and Opalyne Jamoralin
Language: English . . c2005Description: Undergraduate Thesis: (BSCS major in Computer Science) -Pamantasan ng Lungsod ng Maynila, 2005Content type: text Media type: unmediated Carrier type: volumeGenre/Form: academic writingDDC classification: . LOC classification: QA76 L35 2005
Contents:
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.
Summary: 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.
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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.

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.

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