Human face recognition using PCA on wavelet subband

Dissertant

Abd al-Hasan, Husayn Arnus

Thesis advisor

Abbas, Iyad Ibrahim

University

University of Technology

Faculty

-

Department

Department of Electrical Engineering

University Country

Iraq

Degree

Master

Degree Date

2009

English Abstract

Together with the growing interest in the development of human and computer interface and biometric identification, human face recognition has become an active research area since 1990.

Principal Components Analysis (PCA) is a way of identifying patterns in data, and expressing the data in such a way as to highlight their similarities and differences, Since patterns in data can be hard to find in data of high dimension, where the luxury of graphical representation is not available, PC A is a powerful tool for analyzing data, Principal Component Analysis (PCA) has been widely adapted as the most promising face recognition algorithm.

Yet still, PCA has its limitations : poor discriminatory power and large computational load.

In the presence of these limitations, this thesis proposed a method in using PCA; this method applies PCA on wavelet subbands.

Wavelet transform is used to decompose the image into different frequency subbands, where low frequency sub band is used for PCA representation.

The wavelet transform reduces the computational complexity significantly when the image database is large.

In comparison with PCA, which has a recognition rate of (80 %), the proposed method and from the results obtained showed an increase in the recognition rate of up to (85 %).

The results are obtained by using a computer program "MATLAB", Version 7.0 to get better estimation of Human Face Recognition.

Main Subjects

Information Technology and Computer Science

Topics

• Data processing
• Computer software

• APA
• MLA
• AMA

Language

English

Data Type

Arab Theses

Record ID

BIM-305233