A multi-layer perceptron neural network based-model for face detection

Dissertant

al-Qudah, Ghayth Ahmad

Thesis advisor

Shilbayah, Nidal F.

Comitee Members

al-Mamluk, Rustum
al-Shammari, Husayn Hadi Uwayyid
Salamah, Walid

University

Middle East University

Faculty

Faculty of Information Technology

Department

Computer Science Department

University Country

Jordan

Degree

Master

Degree Date

2009

English Abstract

We propose a face detector using an efficient architecture based on a multi layerperceptron neural network (MLPNN) with maximal rejection classifier (MRC) which we achieve enhanced detection accuracy and efficiency using matlab.

First, experimental results show that the proposed neural-network significantly improves the detection accuracy as compared to traditional neural-network-based techniques.

Second, in order to reduce the total computation cost for the face detection, we organize the neural network in this way, simply used at earlier stages are able to reject a majority of non-face patterns in the image backgrounds, thereby significantly improving the overall detection efficiency while maintaining the detection accuracy.

An important advantage of the new architecture is that it has a homogeneous structure so that it is suitable for very efficient implementation using programmable devices.

Our proposed approach achieves one of the best detection accuracies with significantly reduced training and detection cost.

The goal of this project is to detect and locate human faces in a color image.

A set of training images were provided for this purpose.

The objective was to design and implement a face detector in MATLAB and Visual Studio .NET that will detect human faces in an image similar to the training images.

Main Subjects

Information Technology and Computer Science

No. of Pages

49

Table of Contents

• APA
• MLA
• AMA

Language

English

Data Type

Arab Theses

Record ID

BIM-693922