| 000 -LEADER |
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| fixed length control field |
01716nam a22002297a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
CITU |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20210928101840.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
210927b ||||| |||| 00| 0 eng d |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
621.367 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Preferred name for the person |
Lalis, Jeremias T. |
| Relator term |
author |
| 245 ## - TITLE STATEMENT |
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| Title |
Real-time eigenface-based face recognition system / |
| Statement of responsibility, etc |
Jeremias T. Lalis |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
69 leaves : |
| Dimensions |
29 cm. |
| 336 ## - CONTENT TYPE |
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| Source |
rdacontent |
| Content type term |
text |
| Content type code |
txt |
| 337 ## - MEDIA TYPE |
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| Source |
rdamedia |
| Media type term |
unmediated |
| Media type code |
n |
| 338 ## - CARRIER TYPE |
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| Source |
rdacarrier |
| Carrier type term |
volume |
| Carrier type code |
nc |
| 500 ## - GENERAL NOTE |
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| General note |
Computer print-out. |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Thesis (Master in Information Technology) -- Cebu Institute of Technology - University, March 2011. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
The system is composed of five main stages: First, and image of the face is acquired. This Acquisition is accomplished by using a webcam. Second, Viola-Jones method is employed to detect the location of the face in the acquired image and the hue histogram thresholding is used to enable the system to keep track of the face and its features. Third, once the system has targeted a face, the face image is normalized to make it less sensitive to lights variation, head scale and rotation. Fourth, the Principal Component Analysis (PCA) is used to analyze the spatial geometry of distinguishing features of the pre-processed/normalized face image. The final step is to let the eigenface to recognize the face image by looking for the training image that is closest to it in the PCA subspace using nearest-neighbor distance metrics. This is done by calculating Mahalanobis Distance, the distance from the projected test image to each projected training samples. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Image processing |
| General subdivision |
Digital techniques. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Image analysis |
| General subdivision |
Data processing. |
| 942 ## - ADDED ENTRY ELEMENTS |
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| Source of classification or shelving scheme |
|
| Item type |
THESIS / DISSERTATION |