Gender Recognition Using Facial Images

1 Department of Computer Engineering, Fırat University Elazig, Turkey

2 Department of Computer Education and Instructional Technology, Siirt University Siirt, Turkey

Abstract. In this study, gender classification is performed based on front façade photos of 100 male and

100 female. In order to demonstrate the internal face images are aligned and cropped.. Even though some

images are cropped about ears and hairs with the expense of the information loss about gender information at

those parts, the main aim is achieving gender classification on internal face of the human body. It has been

generated that 7 x 200 matrix which obtained from images that include 3 statistical values (average, standard

deviation and entropy) and 4 parameters of GLCM (Gray Level Co-occurrence Matrix). 60% gender

classification accuracy rate is achieved based on the generated frontal face image data set . As a secondary

method, features are extracted by means of GLCM method, followed by application of 2D DWT (The

Discrete wavelet transform) technique on the original images. it has been established attribute of original

images by respectively DWT (The Discrete wavelet transform) and GLCM (Gray Level Co-occurrence

Matrix). When first method is used for 7 photos (7 attitude) which are output 2D DWT, set volume is 49 x

200. Used to 5 different wavelets of relatives and the highest achievement is found at Coiflets Wavelets Filter

by 88%. Second method increases to first method's achievement by %46.

Keywords: Gray Level Co-occurrence Matrix, Discrete wavelet transform, gender classify, facial images

1. Introduction

Gender recognition is the highly effortless cognition among people but it is very complicated process for

the computer. For social life, gender factor undertakes effective role in the communication. Computer based

system in which automatic gender recognition process is a field of the computer vision. This process is

executed with the facial informs or any parts of body which exclude such informs. At the process of facial

informs, due to the fact that peculiar feature of gender such as make-up or beard decrease to similarity ratio,

facilitate to identify and increase reliability and robust of system classify. It has been studied that a lot of

survey for gender recognition with computer system[1]-[5]. Feature extraction and step of classify are

investigated at such studies. Thanks to find knowledge about less but distinctive property, Feature Extraction

Method aids to classify. Basically, classify process is a separated form of a group data where act to similar

mission. Generally, we find two types of gender classify in related studies. One of them is Global Feature-

based and the other is Geometric Feature-based [1]. While Global Feature-based study on the training images,

Geometric Feature-based arrange with about body parts such as nose, ears and hairs[6]. It has been studied

that a lot of survey about method of identifying the gender feature and classifying such methods. These

surveys were started by Golomb [7], Cottrel and Metcalfe [8] at 1990s. In both studies, images are aligned by

hand and later run with the Multi-Layer Perceptron (MLP) directly. Thus, classify process is actualized. Not

to be used Feature Extraction Methods, all pixels of images are adopted as feature and it has been obtained

that classify achievement by %92. Mozaffari et al. [9] have combined global and local feature in their study.

It has been used Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP) for extract to Global

Feature-based that obtained classify achievement by %85. And concluded that female faces are extended and

Corresponding author. Tel.: + (90) 555 417 93 76 ; fax: +(90) 484 223 19 98

E-mail address: serdarabut@gmail.com

2013 International Conference on Agriculture and Biotechnology

IPCBEE vol.60 (2013) © (2013) IACSIT Press, Singapore

DOI: 10.7763/IPCBEE. 2013. V60. 22

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Burhan ERGEN1

and Serdar ABUT2

circular than male faces. Han [10] used to 3D GavabDB for extracts to Geometric Feature-based which is

belonging to face.

It has been determined that male and female’s basic and distinctive feature of faces. Man’s eyebrow is

straight and thick when it compares to female and female have smaller nose than male. GavabDB data set

which consists of 427 images and SVM Classification are used, thus, it has been obtained classify

achievement by %82.56.

Rahman Khorsandi [11], studied on the 2D ears image that is the first study in the gender recognition

fields. He used to Gabor filter for extract feature. In a study, Notre Dame University has obtained classify

achievement by %89.49 in which is used to J Data Group.

In this study, aligned and cropped gender identify is processed. And creation of face database

information will be given at next part. It has been mentioned that feature group in which obtained original

photos in GLCM parameter at part 3. Extract feature is obtained from DWT outcomes as new images and

considered at part 4. Analyse detail of method and compare of each other will be given part 5. And finally it

will be discussed study results and planning future ideas.

2. Creation Of Image Data Set

In this study, against the front of the photos (cropped and aligned 100 male, 100 female) has been used

which are obtained from FEI Face Database. Fig. 1. shows that example FEI faces in the database. While top

row of Fig. 1 shows the male face, the bottom row composes to the female face.

Fig. 1: Some faces in FEI database.

Feature group has been extracted thanks to apply on GLCM from original condition in photos. It has

been extracted that a new feature set which is obtained from new images. Consisting of 2D wavelet

transform with GLCM have been used in this process. When creating of this feature set, 103 filters in which

a relative with 5 different wavelet filter have been extracted for individual feature sets. Providing the highest

performance of the wavelet filter has been searched by iterative methods. For created the two feature set, it

has been used SVM classifier (10 fold CV) and classification performance of these methods have been

compared.

3. Pre-processing

3.1. Discrete Wavelet Transform

DWT has been used in many signal and image processing applications [12]-[15]. An image in which 2D

wavelet transform is expressed to a wavelet function and a scaling function in terms of conversions and

evaluations. This functions are calculated by 2D filter groups where contain a low and a high pass. After 2D

resolution, the image as shown in Fig. 2 is divided to components of multi-resolution in to individual

frequencies.

Sub-bands are referred to as details. k; the scale and J; the largest scale in

the separate. While the original image size is M x N, sub-band size which owns k scale ⁄⁄ . Sub-

band , the lowest resolution residue and j; is selected range from ⁄⁄ and

[16]. Fig. 2 shows sub-bands for a sample photo which are obtained after the 2D Wavelet

Transform. When 2D DWT is applied to an image in data set (Fig. 3), 7 photos is obtained.

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Fig. 2: Sub-bands of 2D

orthogonal Wavelets Transf. Fig. 3: Sub-images are obtained

from a facial image in dtbs

4. Future Extraction

After pre-processing, it has been tried to extract the Gray Level Co-occurrence Matrix of images and

local binary pattern feature.

4.1. Gray Level Co-occurrence Matrix

GLCM is one of the most well known method of texture analysis and it would predict image feature

related to second-order statistics [17]. GLCM is defined as follows: Point (i,j) in GLCM represents frequency

of such pixels that is within a certain window, in the direction of , d from adjacent distance. For d, 1 or 2

is selected. takes four values to and . As an example is shown Fig. 4; pixel values are 1

and 2, for ; frequency value is 3, Gray Level Co-occurrence Matrix of 1 and 2 marks field

value is 3. GLCM's each element is calculated as follows;

∑ ∑

Where; ; frequency , ; direction, d; distance between two adjacent pixels (pixel values are

and )

Fig. 4: An example of GLCM is obtained from an original image.

GLCM reflects to the gray information of images by synthesizing such as level of color and changing in

the direction and distribution [18]. Derived parameters which are obtained by GLCM, can be used for feature

extraction. Haralick et al. have found that 14 varieties form distinguishing feature about based on GLCM

image texture feature which consist of quantitative GLCM description method [19] and [20]. It has been used

4 parameters of GLCM in this study. These parameters include;

Average, standard deviation and entropy values are also added to GLCM feature and 7 features have

been obtained totally. In the study, used 200 photos which for each of them, it has been used above 7 features

and created a data set which is named GLCM. Then, 2D DWT is implemented and it has been created that 7

photos as shown at Fig. 4. It has been extracted 7 GLCM features for each of these 7 photos. So, a total of

7x7 = 49 values for each photo have been obtained. Thus, about 200 photos, it has been created another data

set that consists of 200 x 49 = 9800 features and is named DWT_GLCM.

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Energy: ∑

Homogeneity: ∑ | | ⁄

Contrast : ∑ | |

Correlation: ∑

5. Experimental Results

It has been specified that 4 images of GLCM parameter in first method which mentioned at Chapter 4.1.

In addition, the standard deviation, the entropy and the average values have been calculated. Thus, relating to

200 photos that consist of 100 male and 100 female photos, 7 features have been extracted for each image

and 200 x 7 feature set is created. Then, it is named GSEM feature set, when it is classified as gender and

support from 10-fold SVM (Support Vector Machines), achievement is by %59.5.

It has been tried iterative study about different relative filters of 2D DWT in the second method. Then it

has been classified with the feature set separately. This classification achievements Fig. 5-Fig. 9 are shown

separately.

Fig. 5: Achievements of classify data set which are

created by 45 Daubechies wavelet filters (db1 – db45)

Fig. 7: Achievements of classify data set which are

created by 23 Symlets wavelet filters (sym2 – sym24)

Fig. 9: Achievements of classify data set which are

created by 15 Reverse Biorthogonal wavelet filters

Fig. 6: Achievements of classify data set which are created

by 5 Coiflets wavelet filters (coif1-coif5)

Fig. 8: Achievements of classify data set which are created

by 15 Biorthogonal wavelet filters

Fig. 10: Averages achievement of classify which are created

by 5 wavelet filter families

Averages of classification results show Fig. 10 which are taken by classification of feature sets. Feature

sets are obtained from 103 kinds filter in which 5 wavelet families. Following the implementation of the

various filters to images, coif1 filter is highest result which has been obtained with family Coiflet filter (Fig.

6). 1 is the reset moment value in that scaling and wavelets functions. Thanks to Coif1 filter, gender

classification performance is increased up to 89%. The average is 83% where wavelet transform in all of

these trials.

6. Results and Discussion

0.76

0.78

0.80

0.82

0.84

0.86

0.88

0 20 40

Daubechies wavelets Correct Rate

0.80

0.82

0.84

0.86

0.88

0 5 10 15 20 25

Symlets Correct Rate

0.80

0.82

0.84

0.86

0.88

0 5 10 15

Reverse biorthogonal wavelets Correct Rate

0.80

0.82

0.84

0.86

0.88

0.90

0 2 4 6

Coiflets Correct Rate

0.78

0.80

0.82

0.84

0.86

0 5 10 15

Biorthogonal wavelets Correct Rate

db coif sym bior rbio General 0.79

0.80

0.81

0.82

0.83

0.84

0.85

1

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In these studies, it is investigated that the impact on gender recognition of 2D wavelet transform. GLCM

and 3 statistical values have been applied on the original image and 7 features are studied in all cases. While

200x7 feature set is used in the first method, 200x49 feature set is used in the second method due to 7 photos

in which the outputs of the wavelet transform. While it has been achieved that in 60% with 7 feature in the

first method, the second method has been achieved in 89% with 49 feature. In this way there is provided a 48%

increase in success.

In future studies, thanks to feature selection methods, these 49 DWT features which are created with

different filters will be reduced to 7. In this way, gender characteristics on which applying to wavelet

transform of the image will be measured exactly. Basically, there are two reasons for increase achievement

here. One of them is the expansion of the feature set, the other is the effect of DWT.

At the feature extraction and the classification processes, due to the fact that best performance is aimed

to with at least features, same performance will be struggled in achieve with reducing the number of feature.

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