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1 rnnnrr11 0000086932
AGE INVARIANT FACE RECOGNITION SYSTEM
USING AUTO MA TED VORONOI DIAGRAM SEGMENTATION
NIK NURUL AIN BINTI NIK SUKI
A project report submitted in partial fulfillment of the
requirements for the award of the degree of
Master of Computer Science (Information Security)
Advanced lnformaticSchool
Faculty of Computer Science and Information Systems
Universiti Teknologi Malaysia
JANUARY 2013 U1\iVERSITI MALAYSIA PAHANG
No. Perolehan No. Panggllan
086832
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ABSTRACT
One of the challenges in automatic face recognition is to achieve sequential
face invariant. This is a challenging task because the human face undergoes many
changes as a person grows older. In this study we will be focusing on age invariant
features of a human face. The goal of this study is to investigate the face age invariant
features that can be used for face matching, secondly is to come out with a prototype
of matching scheme that is robust to the changes of facial aging and finally to
evaluate the proposed prototype with the other similar prototype. The proposed
approach is based on automated image segmentation using Voronoi Diagram (VD)
and Delaunay Triangulations (DT). Later from the detected face region, the eyes will
be detected using template matching together with DT. The outcomes, which are list
of five coordinates, will be used to calculate interest distance in human faces. Later
ratios between those distances are formulated. Difference vector will be use in the
proposed method in order to perform face recognition steps. Datasets used for this
research is selected images from FG-NET Aging Database and BioID Face Database,
which is widely being used for image based face aging analysis; consist of 15 sample
images taken from 5 different person. The selection is based on the project scopes
and difference ages. The result shows that 11 images are successfully recognized. It
shows an increase to 73.34% compared to other recent methods.
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ABSTRAK
Salah satu cabaran pemadanan muka secara automatik adalah untuk mencapai
turutan muka yang tidak berubah mengikut masa. Faktor utama yang menyumbang
kepada permasalahan ini adalah kerana wajah manusia mengalami banyak perubahan
sepanjang proses peningkatan usia. Kajian ini akan member tumpuan kepada ciri-ciri
pada wajah manusia yang tidak berubah mengikut umur. Objektif kajian ini adalah
untuk menyiasat ciri-ciri pada wajah yang tidak berubah mengikut umur dan boleh
digunakan untuk memadankan muka, objektif kedua adalah untuk membina prototaip
system pemadanan wajah yang teguh kepada perubahan penuaan wajah dan akhir
sekali adalah untuk menilai prototaip yang dicadangkan dengan prototaip lain yang
hampir serupa. Kajian yang dicadangkan mengunakan segmentasi imej secara
automatik menggunakan Voronoi Diagram (VD) dan Delaunay Triangulations (DT) .
Kemudian daripada imej wajah yang dikesan, mata akan dikesan menggunakan
Template Matching bersama-sama dengan DT. Hasilnya, iaitu lima koordinat, akan
digunakan untuk mengira jarak cirri-ciri penting dalam muka manusia. Kemudian
antara jarak-jarak ini akan dikira. Difference Vector akan digunakan dalam kaedah
yang dicadangkan untuk melaksanakan langkah-langkah pengiktirafan muka. Dataset
yang digunakan di dalam kajian ini merupakan imej-imej yang dipilih dari Pangkalan
Data Penuaan FG-NET dan Pangkalan Data Wajah BioID, yang secara meluas
diterima pakai bagi analisis imej muka berasaskan penuaan. 15 imej sampel yang
diambil daripada 5 orang yang berbeza. Pemilihan adalah berdasarkan skop projek
dan perbezaan umur. Hasilnya menunjukkan bahawa 11 imej berjaya dicam. Ia
menunjukkan peningkatan kepada 73,34% berbanding dengan kaedah lain baru-baru
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TABLE OF CONTENTS
CHAPTER TITLE PAGE
DECLARATION I
DEDICATION 11
ABSTRACT lll
ABSTRAK IV
TABLE OF CONTENTS v
LIST OF TABLES Vlll
LIST OF FIGURES IX
LIST OF ABBREVIATIONS XI
1 INTRODUCTION
1.1 Background Of The Problem 2
1.2 Problem Statement 4
1.3 Project Objectives 4
1.4 Research Questions 5
1.5 Project Aims 5
1.6 Scopes Of The Study 6
2 LITERATURE REVIEW 7
2.1 Face Variations By Age 8
2.2 Image Segmentation 13
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2.2.1 Based on RGB Information 14
2.2.2 Based on Boundary 18
2.2.3 Based on Thresholding 19
2.2.4 Based on Eigen Faces 20
2.2.5 Based on Voronoi Diagram 22
2.3 Features Extraction 23
2.3.1 Chain Codes 23
2.3.2 Template Matching 26
2.4 Distance Metrics 27
2.4.1 Types of Distance 27
2.5 Face Recognition System 30
2.6 Summary 36
3 METHODOLGY 37
3.1 Project Deliverables 38
3.2 Research Operational Framework 39
3.3 Information Gathering and Problem Formulation 41
3.4 Requirements Analysis 41
3.5 Software Design 42
3.5.l Pre-Processing 42
3.5.2 Features Extraction 44
3.5.3 Computation 45
3.5.4 Testing 47
3.6 Implementation 48
3.7 Report Writing 49
3.8 Summary 50
4 DESIGN AND IMPLEMENTATION 51
4.1 Proposed Prototype 52
4.2 Pre-Processing Phase 53
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4.2.1 Image Segmentation Using Voronoi
Tessellations
4.2.2 Selecting Of Probable Face Region
4.3 Features Extraction
4.3.1 Detecting Eyes
4.3.2 Detecting Nose And Mouth
4.4 Face Recognition
4.5 Testing
5 RESULT AND ANALYSIS
5.1 Experimental Result
5.1.l Image Segmentation
5.1.2 Data Calculation
5.1.3 Face Recognition
5.2 Comparison With Other Similar Methods
6 CONCLUSION AND CONTRIBUTION
6.1
6.2
6.3
REFERENCES
APPENDICES
Summary Of The Study
Contribution
Future Works
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LIST OF TABLES
TABLE NO. TITLE PAGE
2.1 Descriptions on human face variations across the age 8
(MAB, 2005)
2.2 Descriptions on four ( 4) key factors that compromise 35
face recognition accuracy.
3.1 Project deliverables throughout the entire process 38
3.2 List of primary global features on human face. 45
3.3 List of the hardware requirements for the system 48
3.4 List of the software requirements for the system 49
4.1 Extracted features with its coordinates 68
5.1 The coordinates (x, y) of Features Extractions 72
5.2 Findings on Distance, Ratios and Standard Deviation 75
5.3 Differences between all tested Images 76
5.4 BiolD _ 0019.jpg test result 78
5.5 Comparison of the proposed method with other 81
methods.
6.1 Project Contributions 84
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LIST OF FIGURES
FIGURE NO. TITLE PAGE
1.1 Typical images with age differences. 3
2.1 Perception of wrinkles at different resolution (in pixels) 11
of a face image at age 50.
2.2 Simple Scenes for Image Segmentations 13
2.3 Illustrations of the HSI Color Space 15
2.4 Segmentation to a colored photo image 16
2.5 Segmentation to the HSI model 17
2.6 Canny edge detected image 18
2.7 Example of Global Thresholding Segmentation 20
2.8 Some example of eigenfaces computed from the ORL 21
dataset
2.9 Example ofDelaunay Triangulation 22
2.10 Direction numbers of a Chain Code 24
2.11 Chain Code Representation 25
2.12 Eyes Template 26
2.13 Manhattan geometry versus Euclidean distance 28
2.14 The reduction in error rate for state-of-the-art face 33
recognition algorithms
2.15 Face images of one subject are shown under various 34
circumstances
3.1 Research Operational Framework 40
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3.2 The proposed block diagram for the face recognition 42
system.
3.4 Illustrations (marked in red color) of the expected 44
outcome from the Features Extraction Step.
3.5 The four ratios used 46
4.1 The main interface for the proposed prototype 52
4.2 The block diagram of the Pre-Processing phase 54
4.3 Delaunay Triangulation of I 0 dot patterns 56
4.4 Comparison of image segmentation using the K-Means 57
and Cheddad A. et al. (2008) method.
4.5 Applying Distance Transformation to separate the face 59
when it is merged with the background
4.6 Face repairing. 60
4.7 The Features Extraction phase 61
4.8 The pseudo code in detecting the eyes 63
4.9 Clustering concept based on Delaunay Triangulations 64
4.10 The proposed ellipse model 65
4.11 The eclipse model 66
4.12 The block diagram of the recognition phase 67
5.1 Extracted Features in BioID _0000.jpg 73
5.2 Difference Vector of tested images 77
5.3 Face Recognition Result 77
5.4 Correct/ False Detection 80
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2D
3D
DT
EU
FEBFRGAC
FERET
FRVT 2002
FRVT 2006
GB
GOP
GWT
HDD
HP
HSI
ICAO
JPG/JPEG
LDA
MBE 2010
MFDA
MLBP
NIST
LIST OF ABBREVIATIONS
Two dimensional
Three dimensional
Delaunay Triangulations
Euclidean
Extraction Based Face Recognition, Gender And Age
Classification
The Facial Recognition Technology Database
Face Recognition Vendor Test 2002
Face Recognition Vendor Test 2006
Giga Byte
Gradient Oriented Pyramid
Gabor Wavelet Transform
Hardisk
Hewlett Packard
Hue Saturation Intensity value
International Civil Aviation Organization
Joint Photographic Experts Group
Linear Discriminant Analysis
Multiple Biometric Evaluation
Multi Features DiscriminatAnalysis
Multi scale local binary pattern
National Institute of Standards and Technology
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PCA
RFID
RGB
SFS
SIFT
SVM
UDP
VD
WLBP
Principle Component Analysis
Radio Frequency Identification
Red, Green and Blue
Shape From Shading
Scale Invariant Feature Transfer
Support Vector Machine
Unsupervised Discriminant Projection
Voronoi Diagram
Walsh-Hadamard transform encoded local binary
patterns
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CHAPTER I
INTRODUCTION
Personal verification and identification is an actively growing area of research
nowadays. Face, voice, lip movements, hand geometry, odor, gait, iris, retina,
fingerprint are the most commonly used authentication methods. These physiological
and behavioral characteristics are known as biometrics, contributed significant
advantages compared to the traditional authentication techniques (Ramesha et al.,
2010). The high demand of the progress in this field is due to the growing role of the
technology in the modem society. As a result, numbers of application appeared in
daily tasks such as online electronic commerce, automated factory machines and
building security system.
Face recognition is one of the biometrics approaches to identify individuals by
the features of the face. Research in this area has been conducted for more than 30
years but only become famous for the past 10 years (Li et al., 2011); therefore,
currently the face recognition technology is well advanced. Wide range of
applications has been using this approach such as security and surveillance system,
human computer interaction, image retrieval, criminal identification, etc. In general,
the approach will be using a pre-stored image database where later the face
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recognition system will be able to identify or verify the person from the captured
image or another source of images to the one in the database. The age and gender of a
person are grouped by visual observation of still images where it is difficult for the
computer vision. Hence, there comes the need to discover the age and gender related
area for further studies. However, in this project, we will be focusing only on age
related issues.
1.1 BACKGROUND OF THE PROBLEM
Face recognition across the ages is a signi.fl~ant problem and has many
applications such as passport photo verification, image retrieval, surveillance (Ling et
al. , 2007), etc. This is a challenging task because the human face undergoes many
changes as a person grows older. Each person has different facial features in many
aspects, including facial texture (e.g. wrinkles), shape (e.g. weight gain), facial hair,
presence of glasses, and exposure to sunlight (Ling et al., 2010), that make us
different to each other. In addition, the image acquisition environments also have an
effect on the images captured.
The performance of face recognition systems cannot challenge with the
sequential changes over a period of time. Law enforcement agencies such as crime
and record bureau regularly require matching a probe image with the individuals in
the missing person database (Singh et al. , 2007), or a customs need to compare new
photo to the previous photo during the passport renewal process. In such applications,
there are many significant differences between the facial features of probe and gallery
image due to age variation. For example, ifthe age of the probe image is 15 years old
and the gallery image of the same person is 5 years old, an existing matching system
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is ineffective and might not produce the expected result. Figure 1 shows several
typical images with different age gaps.
Figure 1.1 Typical images with age differences. Source images are from the FG-
NET Aging Database, http://www.cut.ac.cy/members/fgnet.
One way to overcome this challenge is to regularly update the database with
recent images or templates (Singh et al., 2007). However, this method is not suitable
for applications such as border control, homeland security and missing person
identification. To address this issue, researchers have proposed several techniques
which later we will discuss in Chapter 2.
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1.2 PROBLEMSTATEMENT
Determining age-invariant features of a human face is a difficult task since
human face facing significant changes across time. Still this task is necessary in many
applications, especially those that require checking whether the same person have
issued multiple government documents (e.g. passport, identification card and driving
license) that include facial images (Li et al., 2011 ).
In this project, we will study on standard features or also being referred as
age-invariant features of a human face which later can be used as target points for
face recognition process. To recognize the face, we will come out with a prototype to
determine whether the images come from the same person, in whom an individual is
identified from a large gallery of individuals; in this project we will be using FG-NET
Aging and BioID face database as our dataset.
1.3 PROJECT OBJECTIVES
The objectives of the project are:
1. To investigate age-invariant features of a human face;
2. To develop a prototype for face recognition system based on the selected
features; and
3. To test and evaluate the accuracy of the proposed prototype by companng
with other similar face recognition prototypes.
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1.4 RESEARCH QUESTIONS
The main questions this research motivates to answer are as follows:
1. What are the features of human face that are not changing across age?
2. How to develop a prototype of the face recognition system?
3. What is the effectiveness of the proposed prototype with other similar face
recognition prototype?
1.5 PROJECT AIMS
The aim of this study is to investigate the age-invariant features of a human
face and later develop a prototype of face recognition systems implementing the
selected features. The prototype will be tested using FG-NET Aging and BioID face
database as datasets in our experiments.
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1.6 SCOPES OF THE STUDY
The scopes for this study focus on:
I. Only three (3) age-invariant features will be implemented in the prototype;
2. Image format use for this project will be in Joint Photographic Expert Group
(.jpg and .jpeg) only;
3. Images sample will be a face image with frontal faces only; and
4. The prototype can dealt with color and grayscale images;
5. Sample face images will be taken from FG-NET Aging and BioID face
databases;
6. Age group of the sample images is 18 years old and above.
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CHAPTER2
LITERATURE REVIEW
Human faces are varied with the age group; baby, children, adult and old age.
In this chapter, we will be discussing on the invariant features of human faces. The
need of automated face recognition has been important yet challenging problem. This
challenge can be due to range of variations such as pose, illumination, expression,
and aging. Among these variations, aging factors is now raised attention in the face
recognition community. Designing an age invariant face recognition are crucial in
many applications, especially those that require checking on the person verification
(e.g. passports, driver license) that include facial images. Despite numerous research
efforts has been conducted to study in this area for the past 30 years, most of the
works focus on age estimation and age simulation from the face variations. However
for the past 10 years, the face recognition based on age barrier seems to take
researchers' interest which later will be discussed in this chapter.
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2.1 FACE VARIATIONS BY AGE
According to Marquardt Beauty Analysis, California, the most common
variations of human faces can be grouped into three groups which are age, sex and
geographical (ethnicity). In this project we will be focusing only on the age factor.
The analysis also stated that the periods of ages can be randomly grouped into four
main categories which are babies, children, adults and old age. Table 2.1 explains
further on the variations of the face across the age. Just like every other aspect of
human existence the face also varies with age. To state that the face changes with age
is barely news.
Table 2.1: Descriptions on human face variations across the age (MAB, 2005)
Group Description
Major personal visual attractiveness.
Age group: I - 24 months.
Bigger eyes in proportion to its face.
Rounder face.
Plumper cheeks.
Larger head in portion to its face.
Baby Short, flat eyebrows.
Short, relatively small, turned up nose.
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Table 2.1: Descriptions on human face variations across the age (MAB, 2005)
(continue)
Group
Less attractive.
Age group: 3 - 11 years
Freckles or acne.
Description
Flat eyebrows, fuller than the child.
Ears that appears oversized.
Nose generally small, short and wide.
Children Relatively flatter and less defined cheeks
Adult
Attractive peak.
Age group: 14 to 24 years.
Static facial form.
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Table 2.1: Descriptions on hwnan face variations across the age (MAB, 2005)
(continue)
Group Description
Age group: 25 years and above
The face begins to gradually matures/ aging
The cheeks sag inferiorly resulting the appearance of jowls
The comers of the mouth move inferiorly resulting in a slight frown look
The tissue around the eyes sag inferiorly
The eyelids, upper and lower, themselves sag inferiorly
The tissue of the forehead drifts inferiorly, creating wrinkles and dropping the
eyebrows downward and giving them a flatter appearance
Old Age The nose may lengthen and move the tip inferiorly
The nose may develop a small to pronounced dorsal hwnp
The tip. of the nose may enlarge and become bulbous
Generalized wrinkling of the face may occur
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Human face undergoes significant changes as the person grows older. The
facial features of human are different compared to each other. Singh et al. in their
study divided the human age into three age groups for testing purposes which are 1-
18 years old, 19-40 years old and beyond 41 years old. They strongly believe that
face development depends on the ages of a person. For example, development in
muscles and bone structure caused major changes in the face at the age of 1-18 years
old. From 19-40 years old, the growth rate is comparatively lower, whereas after 40
years old, wrinkles and skin loosening cause major change in facial features and
appearance (Singh et al., 2007).
(a) (b) (c) (d)
Figure 2.1 Perception of wrinkles at different resolution (Ling et al., 2007)
Figure 2.1 illustrate the perception of wrinkles at different resolution (in
pixels) of a face image at age 50.- Image (a)is the original image with height = 347,
image (b) with height = 207, image (c) with height = 160, image (d) with height= 96,
and image (e) with height = 72 pixels. From Figure 2.1, we can clearly see the
wrinkles seem to be disappearing when the resolutions decreases The effects of
wrinkles to the face normalcy are reduced by the image resolution and the natural
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distribution of the wrinkles itself. Kwon (1999), believe that wrinkles are hardly
perceptible for images with low resolution. The distribution of wrinkles on human
faces is known to be inidentical. For example, Boissieux et al. (2007), concluded
eight wrinkles maps from the qualitative data from L'Oreal. From the study, wrinkles
appear frequently on the forehead and cheeks, which are not important for face
recognition. This means that most regions with heavy wrinkles can be ignored for
face recognition.
Earlier study of facial growth conducted by Pittenger and Shaw (1975), has
shown that face profile (e.g. boundary shapes, eye locations) are relatively stable at
the age of 18 years old onward. S. Akazaki et al. (2003), stated that wrinkles are one
of the most important factors for the visual perception of age whereas the width and
depth of wrinkles grow roughly linearly with age.
Considering that entire face with the high structural complexity easily changes
across time, Xu et al. (2011) decided to only use periocular region as one of the age
invariant features for face recognition. This is because the periocular region changes
practically smaller over time due to the shape and location of the eyes remain largely
unchanged compared to mouth, nose, chin, cheek, etc., which are more vulnerable to
changes given by a loosened skin (Masoro and Austad, 2010). These clarify why
most of the researchers select eyes location as a reference point for face recognition.
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2.2 IMAGE SEGMENTATION
Image segmentation can be described as a process of partitioning a digital
image into multiple segments. The goal of image segmentation is to simplify and/ or
change the representation of an image into something that is more meaningful and
easier to analyze (Shapiro G. and Stockman C., 2001). Image segmentation is
normally used to locate objects and boundaries (e.g. lines, curves, etc) in images. The
result from this process is a set of segments extracted from the entire image which
later can be used for object recognition, boundary estimation, image editing, image
database lookup etc (Jepson A. D. and Fleet D. J., 2007). While in this research,
image segmentation is mainly implemented to detect the face region for the ''face
localization" step.
Figure 2.2
Original Image Segmentation (by SMC)
Simple Scenes for Image Segmentations (Jepson A. D. and Fleet D. J. ,
2007)
Figure 2.2 shows a simple example of image segmentation using Spectral
Embedding and Min-Cut (SMC) algorithm. It clearly describes that segmentations of