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ITS » Master Theses » Teknik Informatika - S2
Posted by erna at 03/01/2007 14:59:39  •  6320 Views



Created by :

KeywordAnalisa komponen utama
Basis data


Terdapat banyak metode yang digunakan untuk mengenali wajah-wajah.Template Matching adalah salah satu dari metode yang paling sederhana tetapi metode ini tidak stabil dan sensitif terhadap noise. Pendekatan pada masalah pengenalan wajah di sini adalah menggunakan metode analisis komponen utama (principal components analysis) yang merupakan cara yang lebih baik untuk mencoba mengenali wajah-wajah sesudah beberapa wajah dalam basis data dimasukkan sebagai pelatihan wajah (training face). Informasi yang terdiri dari gambar-gambar wajah (face images) diorientasikan dalam keadaan tampak depan (vertically frontal view) dengan merubah ekspresi wajah. Metode analisis komponen utama (PCA) ini mencoba untuk menentukan komponen utama dari distribusi wajah-wajah atau vektor-vektor karakteristik (eigenvectors) dari matriks kovariansi dari kumpulan gambar-gambar wajah yang telah dinormalisasi. Vektor-vektor karakteristik ini dapat dianggap sebagai kumpulan feature yang mana merupakan karakteristik bersama variasi antar gambar-gambar wajah. Dengan mengacu pada teori yang telah diketahui dengan baik dalam pengenalan pola, kita tahu bahwa masing-masing gambar dalam tahap pelatihan dapat irepresentasikan dengan tepat dalam istilah kombinasi linier dari wajah-wajah karakteristik (eigenfaces). Apabila kita hanya menggunakan beberapa vektor karakteristik yang mempunyai nilai karakteristik (eigenvalues) terbesar, kita dapat mengaproksimasikan dan merepresentasikan variasi yang paling berarti dalam kumpulan gambar-gambar wajah. Implementasi pengenalan wajah dalam tesis ini menggunakan basis data wajah Manchester yang terdiri dari 10 orang dengan 20 foto yang berbeda-beda, yaitu 100 buah foto untuk training dan 100 buah foto untuk testing. Implementasi program pengenalan wajah dengan menggunakan PCA ini dilakukan dengan bantuan compiler Borland Delphi versi 5.0 for Windows dan menghasilkan 90 % wajah yang dikenali.

Alt. Description

There are many methods used to recognize faces. Template matching is one of the simplest method but it is not stable and sensitive to noise. Our approach to the face recognition problem is to use the principal components analysis (PCA). Principal components analysis and neural network methods are the better way to try to recognize faces after some faces in the database have been trained. An information content of face images are vertically oriented frontal view with wide expression change. This principal components analysis method tries to find the principal component of the distribution of faces, or the eigenvectors of the covariance matrix of the set of normalized face images. These eigenvectors can be considered as a set of features which together characterize the variation between face images. By referring to a well-known theory in pattern recognition, we know that each of the images in the training set can be represented exactly in terms of a linear combination of the eigenfaces. If we use only some eigenvectors that have the largest eigenvalues, we can approximate and represent the most significant variation within the face image set. Neural networks method are used to recognize the face through learning correct classification. The implementation of face recognition described in this thesis uses Manchester Face Database that consists of 10 persons with 20 different images : 100 images for training and the other 100 images for testing. The implementation of program of face recognition using PCA is operated using Borland Delphi Compiler version 5. Ofor Windows, the result is 90 % recognized faces.

  1. Dr. Ir. Handayani Tjandrasa, M.Sc
Date Create:03/01/2007
Format:pdf; 87 pages
Collection ID:3100001013051
Call Number:519.535 4 Set

Source :
Theses Informatika Engineering RT 519.535 4 Set, 2000

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