GENDER ESTIMATION BASED ON FACIAL IMAGE AZLIN BT YAJID UNIVERSITI TEKNOLOGI MALAYSIA

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1 GENDER ESTIMATION BASED ON FACIAL IMAGE AZLIN BT YAJID UNIVERSITI TEKNOLOGI MALAYSIA

2 PSZ 19:16 (PIND. 1/97) UNIVERSITI TEKNOLOGI MALAYSIA BORANG PENGESAHAN STATUS TESIS JUDUL: GENDER ESTIMATION BASED ON FACIAL IMAGE SESI PENGAJIAN: 2004 / 2005 Saya AZLIN BINTI YAJID. (HURUF BESAR) mengaku membenarkan tesis (PSM/Sarjana/Doktor Falsafah)* ini disimpan di perpustakaan Universiti Teknologi Malaysia dengan syarat-syarat kegunaan seperti berikut: 1. Tesis adalah hakmilik Universiti Teknologi Malaysia. 2. Perpustakaan Universiti Teknologi Malaysia dibenarkan membuat salinan untuk tujuan pengajian sahaja. 3. Perpustakaan dibenarkan membuat salinan tesis ini sebagai bahan pertukaran antara institusi pengajian tinggi. 4. ** Sila tandakan ( ) SULIT TERHAD Mengandungi maklumat yang berdarjah keselamatan atau kepentingan Malaysia seperti yang termaktub di dalam AKTA RAHSIA RASMI 1972) (Mengandungi maklumat TERHAD yang telah ditentukan oleh organisasi/badan di mana penyelidikan dijalankan) TIDAK TERHAD Disahkan oleh (TANDATANGAN PENULIS) (TANDATANGAN PENYELIA) Alamat Tetap: PT 825, JLN KURNIA JAYA 27, PM DR. SYED ABD. RAHMAN AL-ATTAS TMN KURNIA JAYA,PENG. CHEPA KOTA BHARU,KELANTAN. Nama Penyelia Tarikh: 4 th April Tarikh: 4 th April CATATAN: * Potong yang tidak berkenaan. ** Jika Kertas Projek ini SULIT atau TERHAD, sila lampirkan surat daripada pihak berkuasa/organisasi berkenaan dengan menyatakan sekali sebab dan tempoh kertas projek ini perlu dikelaskan sebagai SULIT dan TERHAD. Tesis dimaksudkan sebagai tesis bagi Ijazah Doktor Falsafah dan Sarjana secara penyelidikan atau disertai bagi pengajian secara kerja kursus dan penyelidikan atau Laporan Projek Sarjana Muda (PSM).

3 I hereby declare that I have read this thesis and in my opinion this thesis is sufficient in terms of scope and quality for the award of the degree of Master of Engineering (Electrical-Electronics & Telecommunication) Signature : Name of Supervisor I : PM DR. SYED ABD. RAHMAN AL-ATTAS Date : 4 th of April 2004

4 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

5 ii I declare that this thesis entitled GENDER ESTIMATION BASED ON FACIAL IMAGE is the results of my own research except as cited in references. This thesis has not been accepted for any degree and is not concurrently submitted in candidature of any degree. Signature : Name of Candidate : Azlin binti Yajid Date : 4 th April 2005

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

7 iv 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.

8 v 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.

9 vi 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.

10 vii LIST OF CONTENTS CHAPTER CONTENT PAGE DECLARATION DEDICATION ACKNOWLEDGEMENTS ABSTRACT ABSTRAK LIST OF CONTENTS LIST OF TABLES LIST OF FIGURES LIST OF NOTATIONS LIST OF EQUATIONS LIST OF ABREVIATIONS LIST OF APPENDICES ii iii iv v vi vii x xi xii xiii xiv xv CHAPTER I INTRODUCTION Introduction to Face Recognition Problem in Face Recognition System Introduction to Gender Estimation Objective 3

11 viii 1.5 Scope of Project Project Outline 4 CHAPTER II LITERATURE REVIEW Introduction Gender Estimation Proposed Processing Techniques Physical Differences Between 9 Genders 2.5 Basic of Image Processing Histogram Equalization Correlation Grayscalling Image Arithmetic Operation 16 CHAPTER III METHODOLOGY Introduction Overall System Development Process Project Flow Hair Detection Ear Detection Template Matching Based on 27 Hairline Shape Template Matching Based on 29 Average Image Template Matching Based on 31

12 ix Facial Shape 3.5 GUI Development 33 CHAPTER IV RESULTS AND DISCUSSIONS Introduction Testing on The Images False Result Analysis on Overall Result Processing Time Discussion 40 CHAPTER V CONCLUSION AND SUMMARY Summary Conclusion Recommendation and Future Works 43 REFERENCES 45 APPENDICES 47-65

13 x LIST OF TABLES TABLE TITLE PAGE 2.1 Feature differences between male and female 9 face 4.1 False detection on hair analysis Overall result of gender estimation system 39

14 xi LIST OF FIGURES FIGURE TITLE PAGE 2.1 Lower part of face Histogram equalization Block diagram of project Hair detection for male Hair detection for female with scarf Detection of ear for bald man Detection of ear for female with white scarf m shape male hairline m shape detection for female Average image template Steps in skin color segmentation for 32 template selection 3.10 Template for facial shape matching Flowchart of GUI Design of GUI figure 35

15 xii LIST OF NOTATIONS ς i ίth Gaussian basis function c i Center σ 2 b ω T(x,y) S(x,y) W H Variance Bias term Weight coefficient Template of an image Region within the image Width dimension Height dimension µ Mean value of the template T µ Mean value of the sub image s M Mask

16 xiii LIST OF EQUATIONS FIGURE TITLE PAGE 2.1 Gaussian Basis Function Correlation Coefficient Image Addition Image Substraction Absolute Difference of two Images Image Multiplication Mean 30

17 xiv LIST OF ABBREVIATIONS GUI Graphical User Interface

18 xv LIST OF APPENDICES APPENDIX TITLE PAGE A Matlab Codes 47 B Function find_color 62 C Function getcolor and make_rgb 64

19 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

20 2 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

21 3 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

22 4 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.

23 5 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.

24 CHAPTER II LITERATURE REVIEW 2.1 Introduction There are a few numbers of proposed methods for gender estimation. They do not consist of image processing techniques only but also applying other type of approach. These will be described briefly in this chapter. 2.2 Gender Estimation Considering face as an image pattern, it is a challenge to detect faces and segment into its specific features, since faces can be very different yet still have the same basic structure and content. 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 and facial features itself. Few attempts have been made to perform gender classification. The most common way of doing this is through these 2 schemes [2]:

25 7 a. Template based approach This simple recognition technique is based on the whole image gray-level templates. The most direct of the matching procedures is the correlation technique. When attempting recognition, the unclassified image is compared with all the database images, returning a vector of matching score. The unknown person is then classified as the one giving the highest cumulative score. b. Feature based approach Procedures that utilize various properties of a face (such as facial topology, hair, etc) as the code for face. The idea is to extract relative position and other parameters of distinctive features such as eyes, mouth, nose and chin.[1,2] 2.3 Proposed Processing Techniques By making use the above approaches various processing techniques have been proposed in previous work. Some well-known method will be described briefly. a. Support Vector Machine (SVM) A Support Vector Machine is a learning algorithm for pattern classification, regression and density estimation. The basic training principle behind SVM s is finding the optimal linear hyper plane such that the expected classification error for unseen test samples is minimized i.e., good generalization performance. [3]

26 8 b. Radial Basis Function Networks A Radial Basis Function (RBF) network is also a kernel-based technique for improved generalization, but it is based instead on regularization theory. A typical RBF network with K Gaussian basis functions is given by K 2 f ( x) = ω ς ( x; c, σ ) b (2.1) i i i i + Where the ς i is the ίth Gaussian basis function with center c i and variance σ 2 i and b is a bias term. The weight coefficient ω i combine the basis functions into single scalar output value, with b as a bias term.[3] c. Principle Component Analysis PCA The main idea in this method is getting the features of the face in mathematical sense instead of the physical face feature by using mathematical transforms [15]. In brief, this algorithm finds the principle components of the covariance matrix of a set of face images. These vectors can be thought of a set of features, which together characterize the variations between human face images. Each face image in the training set can be represented exactly in terms of a linear combination of the eigenfaces.

27 9 2.4 Physical Differences Between Genders Based on medical research and human anatomy it is known that males and females have some distinctions in their physical appearance. However, there also exists some overlapping features. Many handsome actors on close examination have some feminine facial characteristics, while many supermodels have some very male characteristics. Table 2.1 summarize the basic differences between male and female faces but of course the degree of masculinity or femininity varies from person to person. It is important to remember that no single feature makes a face male or female, it is the number of masculine or feminine features that counts [4,9]. Table 2.1: Feature differences between male and female face Facial features Differences Hairline The male hairline is usually higher than the female and tends to have an M shape that recedes at the temples. High hairlines are often seen in beautiful women but again, this must be balanced against the number and degree of any other masculinity. Forehead This is one of the most important gender markers. The ridge of bone that runs right across the forehead just above the eyes (often referred to as brow-bossing ) is almost always far more pronounced in males. In fact many genetic women have almost no discernable brow-bossing at all their foreheads tend to be gently rounded overall with a fairly flat front and in profile they tend to be vertical rather than backwards-sloping. The bossing above the eyes is called the orbital rim and is

28 10 Eyebrows Nose Cheeks Top lip length Lip shape Chin made of solid bone but the bulge towards the middle of the brow ridge covers the hollow area of the frontal sinuses and the bone here can vary a lot in thickness. Male eyebrows tend to be fairly straight and thick and sit right on or just under the orbital rims. Female eyebrows generally sit higher, just above the rims and they usually have a more arched shape. The female nose tends to be smaller, shorter and have a narrower bridge and nostrils than the male one. Also, female noses often have a more concave profile (like a ski jump) and tend to be blunter at the tip but noses can vary a great deal according ethnic background (see Ethnic variations below). Female cheeks tend to be fuller and more rounded than male ones and the cheekbone itself tends to stand a little bit higher and further forward. Men often have hollow cheeks - this is partly because of the flatter cheekbones but also because their cheeks carry less fat. The effect can be emphasised by the fact that the male upper jaw often protrudes forward. The distance between the base of the nose and the top lip is usually longer in males (sometimes a lot longer). When a woman s mouth is relaxed and slightly open she usually shows 2 4 mm of her top teeth. This is considered attractive and also lends a youthful appearance to the face. The whole section of skin between the top lip and nose often has a more backward slope in females. Female lips are often fuller than male ones and tend to be bigger in proportion to the rest of the face. Female mouths also tend to be narrower. Female chins tend to be rounded while male chins tend to be wider with a flat base and two corners to form a more square shape. Male chins are also a lot taller and heavier and are more

29 11 Jaw Adam s apple Overall shape Ethnic variations likely to have a vertical cleft in the middle. Figure 2.1 shows male jawbone is usually much more heavily built throughout than the female one. If one looks at a male face from the front, the bottom third tends to be wider. This is partly because the jawbone itself is wider and partly because the muscles that attach to the corners of the jawbone are much bigger in males. The line of the jaw in females tends to run in a gentle curve from the earlobe to the chin but in males it tends to drop down straight from the ear and then turn at a sharp angle towards the chin giving a square appearance. The Adam s apple is an important male marker because while it is rarely visible in females, it is usually visible in males and can often be very prominent. An attractive female face tends to be roughly heart shaped with the two rounded corners of the hairline at the top coming down to the single point of the chin. The lower third of the male face is usually longer because of the long top lip and the tall chin. Their faces overall have a more square appearance with the two corners of the M shaped hairline at the top coming down to the wide, square cornered jaw at the bottom. In profile the female face tends to be fairly flat while in men the forehead will often slope backwards and the lower half of the face will often protrude forward. Generally the differences between male and female faces mentioned above apply to all ethnic groups. However different ethnic backgrounds can give different advantages and disadvantages for determination of feminisation. For example, people of African descent are likely to already have full lips but may also have a strong brow ridge while people of East Asian descent will often only have a moderate brow ridge but may also have very prominent jaw angles. As mentioned

30 12 before, noses can be very variable between different ethnic groups and this is something that should particularly watch out for. Figure 2.1 : Lower part of face 2.5 Basic of Image Processing This part focuses on some of important image processing techniques applied to this project. Besides there are a few things that need to be take into consideration. One of them is the feature of the image itself. Size, colour, and shape are some commonly used features. Things to be considered are: Feature extraction The features of facial image to be detected and how reliable it is. Most

31 13 features can be computed in two-dimensional images but they are related to three-dimensional characteristics of objects Due to the nature of the image formation process, some features are easily computed reliably while others are very difficult. Feature matching One of the best methods in gender estimation is to do matching based on a model from samples. In most object recognition tasks, there are many features and numerous objects. An exhaustive matching approach will solve the recognition problem but may be too slow to be useful. Effectiveness of features and efficiency of a matching technique must be considered in developing a matching approach Histogram Equalization Histogram equalization is one of the most widely used techniques for many image processing applications. It improves the contrast and the goal of histogram equalization is to obtain a uniform histogram. This technique can be used on a whole image or just on a part of an image. Histogram equalization will not "flatten" a histogram. It redistributes intensity distributions. If the histogram of any image has many peaks and valleys, it will still have peaks and valley after equalization, but peaks and valley will be shifted. Because of this, "spreading" is a better term than "flattening" to describe histogram equalization.

32 14 Because histogram equalization is a point process, new intensities will not be introduced into the image. Existing values will be mapped to new values but the actual number of intensities in the resulting image will be equal or less than the original number of intensities. Figure 2.7 shows the histogram equalization processed on an image. Figure 2.2 : Histogram equalization (a) Original image; (b) Histogram of original image; (c) Equalized image; (d) Histogram of equalized image.

33 Correlation Correlation can be used to determine the existence of a known shape in an image. Classical correlation takes into account the mean of the template and image area under the template as well as the spread of values in both template and image area. That is correlation techniques are applied widely to template matching. Template matching itself is a fundamental approach to face detection. The basic idea of the approach is the computation of statistical similarity between a predefined template of the face and regions within the image.a useful statistical measure is the correlation between the template and a region in the image, which is determined by computing a correlation coefficient. Let T ( x, y) denote the template and S( x, y) denote the region within the image I ( x, y) at which the correlation is computed. T ( x, y) and S( x, y) have the same dimensions, namely, width W and height H. The correlation coefficient R is given by the following equation: R = W x= 0 y= 0 x= 0 y= 0 H W H ( T ( x, y) µ )( S( x, y) µ ) ( T ( x, y) µ ) T 2 T W H x= 0 y= 0 ( S( x, y) µ ) s S 2 (2.2) where µ T is the mean value of the template and µ s is the mean value of the sub image. Regions in the image where a high value of R is obtained are determined to be candidate face regions. Template Matching is a simple approach to face detection, but suffers from the following disadvantages:

34 16 A single template cannot model the large range of variations of the human face. This requires the use of multiple templates, which increases the complexity of the approach. Template Matching is scale-dependent, i.e., it works best when the face region to be detected has a spatial extent very similar to that of the template. If detection at multiple scales is desired, the template needs to be resized, which affects detection performance. If templates at multiple scales are employed to avoid resizing, the computational complexity of the method increases Grayscaling Grayscaling is done to remove the color values of the image by eliminating the hue and saturation information while retaining the luminance. Therefore grayscale can also be called an intensity image. Most of the image processing via Matlab required the image to be in grayscale. Besides grayscale was chosen since it drastically increased the speed of the algorithm Image Arithmetic Operation Image arithmetic is the implementation of standard arithmetic operations, such as addition, subtraction, multiplication, and division, on images. Image arithmetic has

35 17 many uses in image processing both as a preliminary step in more complex operations and by itself. Examples of commonly-used arithmetic operators used between two (or more) images include: Addition (double exposures) Double exposures are a commonly used trick in photography. The effect can be simulated by adding two images together. [ x, y] A[ x, y] + B[ x y] C =, (2.3) There are two common ways of doing this. One is to simply add the two together directly; the other is to add some fraction of one to some fraction of the other. If two images are added together, it s not uncommon for many of the values to exceed the image range (e.g., 255). These values are simply clipped to the maximum value allowed. To avoid this, another approach is to add half of one image to half of the other image. This ensures that the result stays within the range of the image. This allows control to relative weighting of the two images. Subtraction (image differences) A common task in image processing is to determine the difference between two images A and B. This can often be done by subtracting the images:

36 18 [ x, y] = A[ x, y] B[ x y] C, (2.4) Of course, this can produce negative numbers, so a more useful form is to calculate the absolute difference of the two (subtract and then apply an absolute-value operator to the result): C [ x, y] = A[ x, y] B[ x, y] (2.5) Multiplication (masking, transparency) Another arithmetic operation is to multiply one image by another. If one image consists of 0 and 1 value, it acts as a mask M(x,y) on the other image. Those pixels with 0 values produce a black result, and those with a 1value transfer the pixel value for the first image to the output. This can be used to cut out portions of an image. Masking can be combined with addition by using the mask on one image and the inverse mask on the other: [ x, y] = M [ x, y] A[ x, y] + ( 1 M [ x, y] ) B[ x y] C, (2.6) This allows the insertion of part of one image into another. By allowing values between 0 and 1 (i.e., a floating-point image) for the mask values, this allows blending of the two images at the edges of the masked areas to reduce the artifacts of the composition. This approach is called alpha blending because the mask value is usually written as α.

37 19 Averaging multiple images (noise reduction) An extension of image addition is to average multiple images together. This is useful when the images are of the same stationary scene. The changes are the noise.if the noise is itself zero-mean, we can average multiple frames together to reduce the effects of noise. Statistically, this is the same as using the average of multiple samples to provide a better estimate of a population mean

38 CHAPTER III METHODOLOGY 3.1 Introduction This chapter focuses mainly on algorithms used and its implementation in this project. Also included are the review on system requirements and software implementation. 3.2 Overall System This project deals with the software development using Matlab Version on Windows XP platform. The reason of using Matlab is its simplicity and wide range of image processing implementations. The image processing toolbox will be used most. This toolbox is a collection of functions that extend the capability of the Matlab

39 21 numeric-computing environment. The other advantage is that Matlab comes with Graphical User Interface (GUI) toolbox that makes the program implementation easy to use. Other additional software used is Adobe Photoshop Version 6.0.The system requirement for this program to work in acceptable processing time is Pentium II with 128MB RAM or above with at least 4Ghz of hard disk space on Windows platform (Windows 98 and above). Obviously the processing time will vary depending on the processor speed and available memory. 3.3 Development Process The first step of the development part is sampling or facial image collecting. The images are restricted only to facial area with empty background. However no restriction on wears, glasses, make-up, hairstyle, beard, etc are imposed. The images were resized and the backgrounds were removed using Adobe Photoshop. Then only the code writing part begins. At first this program was written based on 40 (20 males and 20 females) facial images and then were tested with additional images that are different from collected images. Details on program development are described in the next part of this chapter.

40 Project Flow Start Colour facial Image RGB to grayscale Histogram equalization Hair detection No Hair Yes No Hair area>50 Yes Average image matching Ear detection Female with scarf No Male Yes Bald Matching base on hairline shape Facial shape matching No Male Female with scarf Template Matching Classifier Done Figure 3.1: Block diagram of project

41 23 Figure 3.1 shows the block diagram of the project. The shaded boxes show the implemented algorithms for classification. To differentiate between female with scarf and male or free hair female is quite an easy task. It becomes a challenge to differentiate between free hair female and male. Therefore multi layer classification is needed as indicated by above figure. Each algorithm applied will be described below. Template matching is chosen as the primary technique in this gender estimation project. Evidently a suitable template is needed which is in effect a general face that has feature of a face without having lots of details. The matching processes consist of many parts as indicated in Figure 3.6 below. The next section will describe briefly each matching technique applied Hair Detection This part is meant to detect presence of hair, which is focused on the upper part of a facial image. To begin this testing the RGB format image is converted to greyscale and then equalized. The processed image is cropped and focused on the upper part of facial image as shown in Figure 3.2 and Figure 3.3. Based on the observation on the samples taken, the color of interest, which is the hair color, is selected and returns a binary image. Pixel value 1 indicates selected pixel and the rest will be 0. Area of on with 1 value pixel is calculated. The greater the value, the more probability of hair area is detected. Again, observation is done to set the threshold in order to differentiate between male and free hair female with female with scarf. The selected threshold is as below.

42 24 Area of on pixel > 1300 = male Area of on pixel < 1300 = female with scarf Area of on pixel <50 = bald or female with white scarf (b) (a) (c) Figure 3.2 : Hair detection for male (a) Male grayscaled image; (b) Region of interest; (c) Detected hair area

43 25 (b) (a) (b) Figure 3.3 : Hair detection for female with scarf (a) Female grayscaled image; (b) Region of interest; (c) Detected hair area. Note that less on pixel compared to Figure 3.2(c) Ear Detection The importance of this part is to differentiate between a female with white scarf and a bald person. There are no probabilities of hair area detected for both cases. So some kind of method is needed to classify them. The easiest way is by doing the ear detection. Obviously no ear will be detected for female with scarf. This part is done simply by selecting small region on the left or right of the image and detect for skin area. This is shown in Figure 3.4 and 3.5.

44 26 (b) (c) (a) Figure 3.4 : Detection of ear for bald man (a) Bald man grayscaled image; (b) Region of interest for ear detection; (c) Detected ear area (b) (c) (a) Figure 3.5 : Detection of ear for female with white scarf (a) Female with white scarf grayscaled image; (b) Region of interest for ear detection; (c) No ear detected

45 Template Matching Based on Hairline Shape Male hairline is also considered in this matching process since most of male has m-shaped hairline. Region or window of interest is set at the hairline area where the processing of the image will be done only to this area. Hair area is selected and the pixels areas are marked as 1. Instead of calculating the area, matching process will be done with the hairline template. The template is taken from one of the male samples that obviously show the m shape of hairline. Figure 3.6 shows some of male hairlines. Note that their hairlines are somehow look like an m shape. However this property is not applied to all male facial shape. Meanwhile Figure 3.7 shows the female hairline is indeed different from the male hairline and of them shows a false detection that classified as male. The result of this matching is in terms of normalized cross correlation of template and facial image. Some threshold is fixed to do classification. Based on the block diagram on Figure 3.1, also note that this algorithm is not applied to female with scarf.

46 Figure 3.6: m shape male hairline 28

47 29 (a) (b) (c) Figure 3.7: m shape detection for female (a) and (b) Female hairline; (c) False detection and classified as male Template Matching Based on Average Image This part is to classify between males and free hair females. For that purpose, a few male facial images were used. Averaging technique was applied here by using average image as a template. Mean of the samples were calculated, using the standard definition of average value:

48 30 mean = 1 N N n= 1 m n (3.1) where N is the number of samples. Average filter is used to remove details such as eyes, nose and mouth. Figure 3.8 shows the normal-looking male images used to form the average image as a template. Figure 3.8(d) is the average results of those images. The details has been removed and left with short hair shape and some eyebrows curvature. By applying the correlation or matching process, this method can fulfilled the mentioned task.

49 31 (a) (b) (c) (d) Figure 3.8 : Average image template (a)-(c) Male facial image to perform average image; (d) Male average image Template Matching Based On Facial Shape This matching technique is needed to refine the classification between male and females. This shape is chosen purely based on observation and try and error of skin part area as a template. The chosen shape is a nice oval shape, which can be found on

50 32 most of female with scarf images. However the free hair females does not have this kind of shape since the hairline that resides at their temples (upper part of head) is more to n shape rather than m shape that is found on man. Figure 3.9 shows steps taken to perform skin color segmentation in order to produce the skin area images. Only then the template is chosen as shown in Figure Create skin color model RGB to YCbCr Noise removing (low pass) Skin region model Skin area detection on image Skin area Skin Segmentation Template Matching Template selection base on observation of collected images Figure 3.9: Steps in skin color segmentation for template selection

51 33 Figure 3.10: Template for facial shape matching 3.5 GUI Development The utmost importance in this project is the ability to classify images based on its gender by going through multilayer classification. However the end user does not need to see the codes or algorithms performed before being presented with the end result. The best way to present the result is by graphical user interface (GUI); which was also built using Matlab The toolbox used is known as Graphical User Interface Development Environment or simply known as GUIDE. Figure 3.11 shows flowchart of the GUI program and Figure 3.12 shows the layout of GUI.

52 34 Start Select image Display name of selected image Load image Display selected image Estimate gender Display result Close End Figure 3.11 : Flowchart of GUI program

53 Figure 3.12 : Design of GUI figure The function of each element is as follows: 1. Image selection from saved images 2. Selected image file will be displayed here 3. Load selected image to image axes (4) 4. Display selected image 5. Invoke gender estimation program 6. Estimated gender will be displayed here 7. Close program

54 CHAPTER IV RESULTS AND DISCUSSIONS 4.1 Introduction To prove that this system is working as expected, the experiment is done using as many facial pictures as possible within mentioned scope. Only then the degree of accuracy of this system can be known. This chapter shows the results of the testing with one by one proposed algorithm. Also it includes results of the overall system when tested with quite a few numbers of images. 4.2 Testing on the Images All of collected facial images were taken either by a digital camera or a scanner.

55 37 These images were resized to 1.6 inches x 2.7 inches and then were tested to the system. Tests were also carried out to the images that were taken from video and television but somehow give unacceptable results. This might be due to the poor illumination and image resolution itself. Therefore only images from digital camera or scanner are taken into consideration. 4.3 False Result Unlike other methods proposed by previous, researchers this project is mainly based on observation of collected samples. Values were recorded then only some comparison will be done and finally threshold is set for classification purpose. However it is not as easy as it sounds. Some samples are harder to classify because of the mixing of male and female features. Some of the false classifications for each test are as follows: i. Hair detection Figure 4.1 shows the examples of false classification based on hair detection. In most of the facial images, this algorithm cannot classify between free hair female and female. The main concept is to do classification by setting up the threshold value as described in Chapter III.

56 38 Table 4.1 : False detection on hair analysis Categories Test Image Processed Image Result (a) Free hair female Male (b) Bald man Female (c) Free hair female Male

57 39 ii. Ear Detection The application of this algorithm is very minima, which is applied only to bald and female with white scarf. Since the appearance of the ear can easily detect for bald man, the error is very minima. iii. Matching There are three types of type of matching mechanism in this system. There are matching based on average image, matching based on facial shape and matching based on shape of hairline. All are applied using correlation. Each of them has problem in classifying between free hair females and males. Figure 3.7(c) for example shows the false detection on female hairline. Instead of male, the m shape was also found on this female facial image. 4.4 Analysis On Overall Result Table 4.3 shows the overall result of the system. The system combines all the proposed algorithms. Note that with this combination a relatively good classifier for gender estimation system has been developed. Out of 56 facial images, this classifier has able to correctly classify 55 facial images, which is 98% of the tested images. Table 4.2 : Overall result of gender estimation system Gender Group Total Images False Estimation Accuracy Male % Free Hair Female % Female With Scarf %

58 Processing time Time taken to successfully recognize and display the result of this system varies depending on what type of algorithms applied to the image. The average estimation time is in the range of 17 to 20 second for every image. With respect to database search system, this program is not so good in terms of processing time. Minimization of the code might be a good solution. Moreover the processing time is also influenced by the machine speeds. 4.6 Discussion There are few problem arise during development of this system. The first and foremost is the data collection part. Most of the samples are taken from UTM students which consist of males, free hair females and females with scarf; taken by digital camera. Due to this, no bald images and no blonde-hair or white hair images were considered. This can be considered as a drawback of this system since there is less variety in samples collection. Nevertheless, considering Asian people the system is adequate. Capturing images from cd or television is not helping much since the quality of the images is not sufficient enough to be implemented. Due to poor illumination, a significant part of the face may not be visible. Internet on other hand is a good source but still some restriction need to be fulfilled like images must be in color and must have a perfect frontal view. Moreover, the quality of some of the downloadable images are not sufficient enough to be applied in this project.

59 41 Studies have shown that facial structure of human face differs between males and females. This founding is known as golden ratio rules. There are some ratio between male and female facial features that differentiate between them. However the implementation of this theory is impossible since slightly different measurement can lead to very much different results. So the best way is by focusing on the obvious features like hair, scarf wearing and facial shape. It seems that estimating woman faces are more difficult than man faces. But the actual problem is to estimate the gender between free hair female and male. The use of special property of male faces like m shape is really helpful in this case.

60 CHAPTER V CONCLUSSION AND SUMMARY 5.1 Summary Gender estimation required multilayer processing. Estimation based on single feature is not adequate. Various image processing techniques were applied and the important of all is the matching process. What had been done in this project is combination of new idea and proposed method by researchers. Unlike the previous one, this project focuses on hairline area and the hair or scarf presence. This will be a great advantage in the sense that it indicates the obvious properties of a gender. Several issues must be addressed like false detection and failure in feature detection. To solve this, various techniques, processing steps and other feature analysis are needed. This is not as easy as it sounds since humans tends to have same features

61 43 property even within the same gender class. Despite all that, gender estimation system is very helpful in enhancing facial recognition system that is already being applied today. 5.2 Conclusions In general, the objectives of this project have been achieved. However the most important thing to be considered is a good image quality. The accuracy of this system can be achieved up to 98%. Although the algorithms proposed in this project have been designed for a specific application, its philosophy is quite general and can be extended easily for other application. There is still room for algorithms refinement, particularly on using other facial features for gender estimation 5.3 Recommendation for Future Works This system has a great potential in sense that it can be extended to various usages especially in image recognition and security area. However the weakness of this program is it is very sensitive to image quality and size since most of algorithms concern with matching process.

62 44 Instead of applying this project to static facial image, it seems to be much more useful if it can be extended to real time application where there is no restriction to image angle or background images. Some modification therefore is needed especially in automatic detection of facial features like hairlines or hair. Same algorithms can still be applied to achieve a desired result. Although this project produces quite a good classifier, the error will increase as number of test images increase. Perhaps the best solution is by considering other facial features to classify the images. Finally, to broaden the usage of this project other human facial features also need to be considered. For example, having white, coloured or blond hair and wearing caps. Perhaps cases of transvestite can be seen as new part of research by doing some analysis on various features in order to estimate the gender.

63 45 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] [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, ,1995 [7] Forchheimer, R., Mu, F., Li, H., Automatic Extraction Of Human Facial Features, Signal Process. Imag. Comm. 8, , [8] J.Hayashi, M.Yasumoto, H.Ito, Y.Niwa, H.Koshimizu, Age and Gender Estimation

64 46 from Facial Image Processing, SICE 2003, 5-7,2002 [9]

65 47 APPENDIX A Matlab Codes function varargout = gui(varargin) % GUI M-file for gui.fig % GUI, by itself, creates a new GUI or raises the existing % singleton*. % % H = GUI returns the handle to a new GUI or the handle to % the existing singleton*. % % GUI('CALLBACK',hObject,eventData,handles,...) calls the local % function named CALLBACK in GUI.M with the given input arguments. % % GUI('Property','Value',...) creates a new GUI or raises the % existing singleton*. Starting from the left, property value pairs are % applied to the GUI before gui_openingfunction gets called. An % unrecognized property name or invalid value makes property application % stop. All inputs are passed to gui_openingfcn via varargin. % % *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one % instance to run (singleton)".

66 48 % % See also: GUIDE, GUIDATA, GUIHANDLES % Edit the above text to modify the response to help gui % Last Modified by GUIDE v Feb :01:10 % Begin initialization code - DO NOT EDIT gui_singleton = 1; gui_state = struct('gui_name', mfilename,... 'gui_singleton', gui_singleton,... 'gui_layoutfcn', [],... 'gui_callback', []); if nargin & isstr(varargin{1}) gui_state.gui_callback = str2func(varargin{1}); end if nargout [varargout{1:nargout}] = gui_mainfcn(gui_state, varargin{:}); else gui_mainfcn(gui_state, varargin{:}); end % End initialization code - DO NOT EDIT % --- Executes just before gui is made visible. function gui_openingfcn(hobject, eventdata, handles, varargin) set(gcf,'color',[0.502,0,0.251]) % This function has no output args, see OutputFcn. % hobject handle to figure

67 49 % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) % varargin command line arguments to gui (see VARARGIN) image_file = get(handles.inputedit,'string'); if ~isempty(image_file) im_original=imread(char(image_file)); %Using Matlab function to read the image. set(handles.imageaxes,'handlevisibility','on'); %Active the axes for image viewing axes(handles.imageaxes); % Show the image in the axes image(im_original); %normal Matlab function to show image axis equal; %sets the aspect ratio. (Show the image in its right ratio) axis tight; %Sets the axis limits to the arrange of the data. axis off; % Turn off all axis labeling %After showed the image, let orgim to be unchangeable until the commands setit 'on' set(handles.imageaxes,'handlevisibility','off'); end; % pair with 'if' % Choose default command line output for gui handles.output = hobject; % Update handles structure guidata(hobject, handles); % UIWAIT makes gui wait for user response (see UIRESUME) % uiwait(handles.figure1); % --- Outputs from this function are returned to the command line. function varargout = gui_outputfcn(hobject, eventdata, handles) % varargout cell array for returning output args (see VARARGOUT);

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