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Comparison of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) Based Feature Extraction for Face Recognition System and Implementation for Biometrics Based Time Attendence System

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Face Recognition begins with extracting the coordinates of features such as width of mouth, width of eyes, pupil, and compare the result with the measurements stored in the database and return the closest record. Nowadays, there are a lot of face recognition technique and algorithms found and developed around the world. Facial recognition becomes an interesting research topic. It is proven by numerous number of published papers related with facial recognition including facial feature extraction, facial algorithm improvements, and facial recognition implementations. Main purposes of this research are to get the best facial recognition algorithm (Eigenface uses PCA and Fisherface uses LDA) provided by the Open CV 2.4.8 by comparing the ROC (Receiver Operating Characteristics) curve and implement it in the attendance system as the main case study. Based on the experiments, the ROC curve proves that Eigenface produce better recognition results with less error rate than the Fisherface. Eigenface implemented inside the Attendance System returns between 70% to 90% similarity for genuine face images.

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Availability

B01609

(Rack Thesis)

Available but not for loan - Missing

Detail Information

Series Title

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Call Number

1609

Publisher

: Swiss German University., 2014

Collation

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Language

English

ISBN/ISSN

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Classification

NONE

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

IT
OpenCV
Face recognition
biometrics
Eigenface
Fisherface
Attendance system

Specific Detail Info

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Statement of Responsibility

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Other version/related

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