![](/images/graphics-bg.png)
Ethnic classification of face images using inductive learning
Dissertant
Thesis advisor
Comitee Members
al-Qaddumi, Ashraf Ahmad
Salamah, Walid
al-Dawud, Ali
University
Princess Sumaya University for Technology
Faculty
King Hussein Faculty for Computing Sciences
Department
Department of Computer Sciences
University Country
Jordan
Degree
Master
Degree Date
2014
English Abstract
This thesis provides the framework of new research in regards to racial classification of human race, taking into consideration Inductive Learning Algorithm (ILA) one of the machine learning algorithms. Experiments show that this approach has significant importance and results comparable to other approaches as the use of image treatments or neural networks. The focus of the thesis is divided into two phases: A - To sort out racial classification into three races, (Arab, Asian and Caucasian) in the evaluation part of the thesis compared with our work will be proved to complete previous work as results that used neural networks. B- Negro race was added as a new race , which has many various unique characteristics compared with other race such as lips and nose. The mechanism of both phases were the same, implementing the same collection of human faces images have been taken in previous research using neural network (NN), and pictures of (40 Arabic, 43 Asian and 67 Caucasian) in addition to 70 pictures of Negro race. The methodology of the experiment is based on submitting all faces details manually into Excel spreadsheet, each column contains certain characteristics that refer to face detail as a question or drop down list of options, thereafter, second part of this experiment was inserting these data into the inductive learning algorithm (ILA). The used dataset is divided into a training set and test (unseen) set, the main objective of training set is to build a model (generated rules), while the test set is to validate the built model.
The remaining part of the dataset was used for the test.
Obtained result from the first phase of three races was 86.36 percent, which was more accurate than the previous results of neural networks that was 83.5 percent. But after the addition of another race (black), the result was slightly less accurate than the previous result, which was 82.63 percent, and this shows the power and great ability of (ILA) in classification human faces through both phases.
Main Subjects
Information Technology and Computer Science
Topics
No. of Pages
120
Table of Contents
Table of contents.
Abstract.
Chapter One : Introduction.
Chapter Two : Related works and backgrounds.
Chapter Three : Inductive learning.
Chapter Four : Proposed methodology.
Chapter Five : Experimental results.
Chapter Six : Summary and conclusion.
References.
American Psychological Association (APA)
al-Hiti, Khaldun Abd Allah. (2014). Ethnic classification of face images using inductive learning. (Master's theses Theses and Dissertations Master). Princess Sumaya University for Technology, Jordan
https://search.emarefa.net/detail/BIM-414241
Modern Language Association (MLA)
al-Hiti, Khaldun Abd Allah. Ethnic classification of face images using inductive learning. (Master's theses Theses and Dissertations Master). Princess Sumaya University for Technology. (2014).
https://search.emarefa.net/detail/BIM-414241
American Medical Association (AMA)
al-Hiti, Khaldun Abd Allah. (2014). Ethnic classification of face images using inductive learning. (Master's theses Theses and Dissertations Master). Princess Sumaya University for Technology, Jordan
https://search.emarefa.net/detail/BIM-414241
Language
English
Data Type
Arab Theses
Record ID
BIM-414241