Siswanto, Adrian Rhesa Septian and Nugroho, Anto S. and Galinium, Maulahikmah (2014) Comparison of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) Based Feature Extraction for Face Recognition System and Implementation for Biometrics Based Time Attendance System. Bachelor thesis, Swiss German University.

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Abstract

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 numbers 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.

Item Type:	Thesis (Bachelor)
Uncontrolled Keywords:	Face Recognition, Open CV, Eigenface, Fisherface, Attendance System, Biometrics
Subjects:	T Technology > T Technology (General) > T58.5 Information technology T Technology > TA Engineering (General). Civil engineering (General) > TA1653 Human face recognition (Computer science) T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7882 Biometric identification
Divisions:	Faculty of Engineering and Information Technology > Department of Information Technology
Depositing User:	Faisal Ifzaldi
Date Deposited:	04 May 2021 15:29
Last Modified:	04 May 2021 15:29
URI:	http://repository.sgu.ac.id/id/eprint/1997

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