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Human face detection and recognition / Espiritu, Christine Joy R. and Singson, Aaron James L. 6

By: Espiritu, Christine Joy R. and Singson, Aaron James L. 4 0 16, [, ] | [, ] |

Contributor(s): 5 6 [] |

Language: Unknown language code Summary language: Unknown language code Original language: Unknown language code Series: ; March 2004.46Edition: Description: 28 cmContent type: text Media type: unmediated Carrier type: volumeISBN: ISSN: 2Other title: 6 []Uniform titles: | | Related works: 1 40 6 []Subject(s): -- 2 -- 0 -- -- | -- 2 -- 0 -- 6 -- | 2 0 -- | -- -- 20 -- | | -- -- -- -- 20 -- | -- -- -- 20 -- --Genre/Form: -- 2 -- Additional physical formats: DDC classification: | LOC classification: | | 2Other classification:

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Action note: In: Summary: ABSTRACT: The study of Human Face Detection and Recognition of the proponents was inspired by the thesis Human Face Detector created by the previous proponents namely, Janario Jiao and Mary Ann Guelas. This paper is about an attempt to unravel the classical problem of automated human face detection and recognition. A near real-time, fully automated computer vision system was developed to detect faces and recognize them. In the implemented system, automated face detection was achieved using Eigenfaces and Guassian Pyramids Algorithm. The natural symmetry of the face in search of the exact face location. Once the location of the face in an image was known, this pixel region was extracted and the test subject was recognized using principal component analysis, also known as the eigenface approach. Other editions:

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Item type	Current location	Home library	Collection	Call number	Status	Date due	Barcode	Item holds
Book	PLM	PLM Archives	Filipiniana-Thesis	T QA76.76 .E87 2004 (Browse shelf)	Available		FT3697

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Undergraduate Thesis: (Bachelor of in Computer Science) - Pamantasan ng Lungsod ng Maynila, 2004. 56

ABSTRACT: The study of Human Face Detection and Recognition of the proponents was inspired by the thesis Human Face Detector created by the previous proponents namely, Janario Jiao and Mary Ann Guelas. This paper is about an attempt to unravel the classical problem of automated human face detection and recognition. A near real-time, fully automated computer vision system was developed to detect faces and recognize them. In the implemented system, automated face detection was achieved using Eigenfaces and Guassian Pyramids Algorithm. The natural symmetry of the face in search of the exact face location. Once the location of the face in an image was known, this pixel region was extracted and the test subject was recognized using principal component analysis, also known as the eigenface approach.

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