The effectiveness of haar features and threshold in multiple face detection systems based on adaboost algorithm
Other Title(s)
فعالية كل من مزايا الهار و العتبة في أنظمة اكتشاف الوجه المتعددة اعتمادا على خوارزمية Adaboost
Dissertant
al-Nuri, Khadijah Mustafa Khalil
Thesis advisor
Comitee Members
Hassan, Muhammad M. al-Haj
Yahya, Abd al-Fattah A.
University
Middle East University
Faculty
Faculty of Information Technology
Department
Computer Science Department
University Country
Jordan
Degree
Master
Degree Date
2011
English Abstract
The problem of face detection is still a standing problem in the research area.
One of the most famous methods that is successful is the Viola & Jones approach.
In this thesis, systems were designed based on this approach to measure the effectiveness of the different Haar feature types, and to compare two types of threshold computing methods.
There are 8 different Haar features, which can make 6 systems that would contain 4, 4, 5, 6, 7, or 8 of these Haar features.
The two methods used for computing thresholds are the average of means and the optimal threshold methods.
The implemented systems have been trained using a handpicked database.
The database contains 350 face and nonface images.
Adaboost algorithm has been used to build our detectors .
Each detector consists of 3 cascade stages.
In each stage , we randomly use a number of weak classifiers to build the strong classifier.
Each weak classifier is computed based on threshold before entering the Adaboost algorithm.
If the image can pass through all stages of the detector, then the face will be detected.
The detectors have been tested using the MIT+CUM database.
Some recommendations have been suggested according to the Haar features and the computed threshold to improve the face detection of Viola Jones approach.
Main Subjects
Information Technology and Computer Science
Topics
No. of Pages
80
Table of Contents
Table of contents.
Abstract.
Abstract in Arabic.
Chapter One : Introduction.
Chapter Two : Literature review.
Chapter Three : Face detection system using haar features and adaboost algorithm.
Chapter Four : Experimental result and discussion.
Chapter Five : Conclusion and future work.
References.
American Psychological Association (APA)
al-Nuri, Khadijah Mustafa Khalil. (2011). The effectiveness of haar features and threshold in multiple face detection systems based on adaboost algorithm. (Master's theses Theses and Dissertations Master). Middle East University, Jordan
https://search.emarefa.net/detail/BIM-700182
Modern Language Association (MLA)
al-Nuri, Khadijah Mustafa Khalil. The effectiveness of haar features and threshold in multiple face detection systems based on adaboost algorithm. (Master's theses Theses and Dissertations Master). Middle East University. (2011).
https://search.emarefa.net/detail/BIM-700182
American Medical Association (AMA)
al-Nuri, Khadijah Mustafa Khalil. (2011). The effectiveness of haar features and threshold in multiple face detection systems based on adaboost algorithm. (Master's theses Theses and Dissertations Master). Middle East University, Jordan
https://search.emarefa.net/detail/BIM-700182
Language
English
Data Type
Arab Theses
Record ID
BIM-700182
