Ethnic classification of face images using inductive learning
مقدم أطروحة جامعية
مشرف أطروحة جامعية
أعضاء اللجنة
al-Qaddumi, Ashraf Ahmad
Salamah, Walid
al-Dawud, Ali
الجامعة
جامعة الأميرة سمية للتكنولوجيا
الكلية
كلية الملك الحسين لعلوم الحوسبة
القسم الأكاديمي
قسم علم الحاسوب
دولة الجامعة
الأردن
الدرجة العلمية
ماجستير
تاريخ الدرجة العلمية
2014
الملخص الإنجليزي
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.
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الموضوعات
عدد الصفحات
120
قائمة المحتويات
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.
نمط استشهاد جمعية علماء النفس الأمريكية (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
نمط استشهاد الجمعية الأمريكية للغات الحديثة (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
نمط استشهاد الجمعية الطبية الأمريكية (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
لغة النص
الإنجليزية
نوع البيانات
رسائل جامعية
رقم السجل
BIM-414241
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر