GENDER ESTIMATION BASED ON FACIAL IMAGE

AZLIN BT YAJID

UNIVERSITI TEKNOLOGI MALAYSIA

GENDER ESTIMATION BASED ON FACIAL IMAGE

AZLIN BINTI YAJID

A dissertation submitted in partial fulfillment

of the requirements for the award of the degree

of Master of Engineering

(Electrical-Electronics & Telecommunication)

Faculty of Electrical Engineering

Universiti Teknologi Malaysia

APRIL, 2005

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Specially dedicated to my family for their supports and eternal love.

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ACKNOWLEDGEMENTS

Praise to Allah, the Most Gracious and Most Merciful, Who has created the

mankind with knowledge, wisdom and power.

First of all, the author would like to express his deepest gratitude to Associate

Professor Dr. Syed Abd. Rahman Al-Attas for his continuous support, ideas, supervision

and encouragement during the course of this project. The author would not have

completed this project successfully without his assistance.

The author is thankful to Mr Anuar Zaini and wife, Mr. Mohamad Nansah, Ms.

Syakira, Ms. Norasiah and Ms. Ismahani for advice and helpful cooperation during the

period of this research. Appreciation is also acknowledged to those who have

contributed directly or indirectly in the completion of this project.

The author would also like to extend his appreciation to his family members,

for their support, patience and endless love.

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ABSTRACT

Although gender classification has attracted much attention in psychological

literature, relatively few machine vision methods has been proposed. However it has

been extensively studied in the context of surveillance applications and biometrics. This

project is mainly concern with gender classification using purely image processing

technique. The way of doing this is by extracting the differences between male and

female facial features. Obviously the classification base on a single feature is not

adequate since humans share many facial properties even within different gender group.

So multilayer processing is needed. This project is working as expected with specified

scope of project. Although not many varieties of facial images have been considered like

colored hair the basic techniques should be just the same. The proposed methods can be

extended to various purposes especially in speeding up the processing time in database

searching. The refinement of this project in other hand can lead to more accurate and

reliable result by considering other facial properties like eyes, nose and eyebrows.

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ABSTRAK

Bidang pengecaman jantina telah menjadi satu topik yang diberikan perhatian

dalam pengajian psikologi. Namun begitu yang sedikit pendekatan melalui teknik

pengelihatanyang telah diperkenalkan. Bidang ini sebenarnya telah dipelajari secara

mendalam dalam konteks keselamatan dan biometrik. Projek ini adalah berkisar tentang

pengecaman jantina melalui teknik pemprosesan imej semata-mata. Ini dilakukan

dengan mengenalpasti perbezaan di antara ciri-ciri muka lelaki dengan perempuan.

Adalah terbukti bahawa pengkelasan berdasarkan satu ciri sahaja adalah tidah tepat

memandangkan manusia mempunyai ciri-ciri muka yang hampir sama walaupun dari

kelas jantina yang berbeza. Oleh kerana itu pengkelasan secara berperingkat diperlukan.

Projek ini berjaya sepertimana yang diharapkan; berdasarkan skop yang telah ditetapkan.

Walaupun tidak banyak jenis-jenis muka yang diambil kira seperti warna rambut yang

berlainan dari asal, teknik yang digunakan sepatutnya masih lagi sama. Kegunaan projek

ini boleh dikembangkan kepada pelbagai tujuan terumanya untuk mempercepatkan

process pencarian dalam pangkalan data. Dengan sedikit pengubahsusian, projek ini

semestinya akan menghasilkan satu system yang lebih tepat; dengan mengambil kira

ciri-ciri muka manusia yang lain seperti mata, hidung dan kening.

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LIST OF CONTENTS

CHAPTER CONTENT PAGE

DECLARATION ii

DEDICATION iii

ACKNOWLEDGEMENTS iv

ABSTRACT v

ABSTRAK vi

LIST OF CONTENTS vii

LIST OF TABLES x

LIST OF FIGURES xi

LIST OF NOTATIONS xii

LIST OF EQUATIONS xiii

LIST OF ABREVIATIONS xiv

LIST OF APPENDICES xv

CHAPTER I INTRODUCTION 1

1.1 Introduction to Face Recognition 1

1.2 Problem in Face Recognition System 2

1.3 Introduction to Gender Estimation 2

1.4 Objective 3

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1.5 Scope of Project 3

1.6 Project Outline 4

CHAPTER II LITERATURE REVIEW 5

2.1 Introduction 5

2.2 Gender Estimation 5

2.3 Proposed Processing Techniques 5

2.4 Physical Differences Between

Genders

9

2.5 Basic of Image Processing 12

2.5.1 Histogram Equalization 13

2.5.2 Correlation 15

2.5.3 Grayscalling 16

2.5.4 Image Arithmetic Operation 16

CHAPTER III METHODOLOGY 20

3.1 Introduction 20

3.2 Overall System 20

3.3 Development Process 21

3.4 Project Flow 22

3.4.1 Hair Detection 23

3.4.2 Ear Detection 25

3.4.3 Template Matching Based on

Hairline Shape

27

3.4.4 Template Matching Based on

Average Image

29

3.4.5 Template Matching Based on 31

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Facial Shape

3.5 GUI Development 33

CHAPTER IV RESULTS AND DISCUSSIONS 36

4.1 Introduction 36

4.2 Testing on The Images 36

4.3 False Result 37

4.4 Analysis on Overall Result 39

4.5 Processing Time 40

4.6 Discussion 40

CHAPTER V CONCLUSION AND SUMMARY 42

5.1 Summary 42

5.2 Conclusion 43

5.3 Recommendation and Future Works 43

REFERENCES 45

APPENDICES 47-65

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LIST OF TABLES

TABLE TITLE PAGE

2.1 Feature differences between male and female

face

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4.1 False detection on hair analysis 38

4.2 Overall result of gender estimation system 39

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LIST OF FIGURES

FIGURE TITLE PAGE

2.1 Lower part of face 12

2.2 Histogram equalization 14

3.1 Block diagram of project 22

3.2 Hair detection for male 24

3.3 Hair detection for female with scarf 25

3.4 Detection of ear for bald man 26

3.5 Detection of ear for female with white scarf 26

3.6 ‘m’ shape male hairline 28

3.7 ‘m’ shape detection for female 29

3.8 Average image template 31

3.9 Steps in skin color segmentation for

template selection

32

3.10 Template for facial shape matching 33

3.11 Flowchart of GUI 34

3.12 Design of GUI figure 35

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LIST OF NOTATIONS

ςi ίth Gaussian basis function

ci Center

σ2 Variance

b Bias term

ω Weight coefficient

T(x,y) Template of an image

S(x,y) Region within the image

W Width dimension

H Height dimension

Tµ Mean value of the template

sµ Mean value of the sub image

M Mask

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LIST OF EQUATIONS

FIGURE TITLE PAGE

2.1 Gaussian Basis Function 8

2.2 Correlation Coefficient 15

2.3 Image Addition 17

2.4 Image Substraction 18

2.5 Absolute Difference of two Images 18

2.6 Image Multiplication 18

3.1 Mean 30

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LIST OF ABBREVIATIONS

GUI Graphical User Interface

xv

LIST OF APPENDICES

APPENDIX TITLE PAGE

A Matlab Codes 47

B Function find_color 62

C Function getcolor and make_rgb 64

CHAPTER I

INTRODUCTION

1.1 Introduction to Face Recognition

Face is one of the most important biometric features of a human. A human can

recognize different faces without difficulty. Yet it is a challenging task to design a robust

computer system for face identification. The inadequacy of automated face recognition

systems is especially apparent when compared to our own innate face recognition

ability. Human perform face recognition, an extremely complex visual task, almost

instantaneously and our own recognition ability is far more robust than any computer's

can hope to be. Human can recognize a familiar individual under very adverse lighting

conditions, from varying angles or viewpoints.

While research into this area dates back to the 1960's, it is only very recently that

acceptable results have been obtained. However, face recognition is still an area of

active research since a completely successful approach or model has not been proposed

to solve the face recognition problem. The next generation surveillance systems are

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expected to take human face as input pattern and extract useful information such as

gender information from it.

1.2 Problem in Face Recognition System

To date, most face recognition systems have had at most a few hundred faces.

This could be a problem when the size of database increases. Larger database means

longer computational and processing time. The identification of gender can help the face

recognition system to focus more on the identity related features, and limit the number

of entries to be searched in a large database, improving the search speed In other words

estimation will be done on the input image and recognition of image is done only in the

estimation group. Theoretically this method will cut the processing time almost to half.

1.3 Introduction to Gender Estimation

Gender classification based on facial images is difficult mostly because of the

inherent variability of the image formation process in terms of image quality and

photometry, geometry, and/or occlusion, change, and disguise. Few attempts have been

made to perform gender classification starting in the early 1990s where various neural

network techniques were employed for classifying the gender of a (frontal) face.

The interest on gender estimation has two folds. First, one can apply the gender

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estimation procedure prior to face recognition in order to split the face space into two.

Second, because of the nature of the problem, one can apply same methodology to other

class specific face processing tasks like race and age estimation. Thus, by arriving at a

robust gender estimation scheme, one can hope to propose solutions to similar face tasks

as well.

1.4 Objective

One of the most challenging tasks for visual form (’shape’) analysis and object

recognition is the understanding of how people process and recognize each other’s face,

and the development of corresponding computational models. The objective of this

project is therefore to write a Matlab code in such a way that it can recognize the gender

of a person from given frontal image. The algorithm will be a combination of various

proposed method along with some other features . Finally, this project hopefully can be

a relatively good gender classifier as other proposed methods.

1.5 Scope of Project

Gender classification of a person based on only a frontal view image is

something a human can easily accomplish. It can be decided by the person’s hair, nose,

eyes, mouth and other properties with relatively high degree of accuracy. However this

will be a problem when it comes to automating the processing using a computer

program. This project therefore is to solve this matter. The gender estimation algorithm

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will be done via Matlab image processing tools. In this project it is assumed that the

background of the facial image is not complex and there is only a single face on it.

Further each image is assumed in a same size, the image quality and resolution is

assumed to be sufficient enough, the illumination is uniformed and the input images are

colour images. Transvestite (male/female that change the appearance to opposite sex) is

not considered in this project. However no restriction on wears, glasses, make-up,

hairstyle, beard, etc imposed

1.6 Project Outline

The project is organized into six chapters. The outline is as follows;

Chapter 1 - Introduction

This chapter discusses the objectives and scope of the project and gives a

general introduction to facial recognition and gender estimation technology.

Chapter 2 - Literature Review

This chapter is about previous work regarding the facial detection, facial feature

extraction and gender estimation. A few techniques will be reviewed briefly.

Major differences between male and female facial feature will be described.

Lastly some of important image processing technique will be discussed.

Chapter 3- Methodology

Chapter 3 elaborates the techniques and steps taken to complete the task. A few

algorithms is proposed to be applied in this project.

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Chapter 4- Results

The final result of this project are shown and discussed in this chapter. Some

analysis of the results and each algorithm applied are also included.

Chapter 5-Conclussion

This chapter consists of conclusion for this project. It also describe the problems

arises and suggestion for future improvement and works.

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REFERENCES

[1] Laurenz Wiskott et al. “Face Recognition and Gender Determination”

[2] Brunelli and Poggio, “ Face Reconition: Features Versus Templates”, IEEE

Transaction on Pattern Analysis and Machine Intellegence, Vol 15,No 10,October

1993

[3] B. Moghaddam and M.H. Yang’ “Learning Gender With Support Faces”, IEEE

Transaction on Pattern Analysis and Machine Intellegence, Vol 24,No 5,May 2002

[4] http://files.frashii.com/~lisa/annierichards.coolfreepage.com/skeleton.htm

[5] Selin Baskan,M.Mete Bulkun,Volkan Atalay,” Projection Based Method For

Segmentation Of Human Face And Its Evaluation”, Pattern Recognition Letters 23,

2002

[6] Chellappa, R., Wilson, C.L., Sirohey, S., “Human And Machine Recognition Of

Faces: A Survey”,Proc. IEEE 83, 705–740,1995

[7] Forchheimer, R., Mu, F., Li, H., “Automatic Extraction Of Human Facial Features”,

Signal Process. Imag. Comm. 8, 309–332, 1996.

[8] J.Hayashi, M.Yasumoto, H.Ito, Y.Niwa, H.Koshimizu, “Age and Gender Estimation

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from Facial Image Processing”, SICE 2003, 5-7,2002

[9] http://www.virtualffs.co.uk