Face Detection Based on Adaptive Skin Color Model and Geometric Features

Guoliang Yang School of Electrical Engineering & Automation

Jiangxi University of Science and Technology, JXUST Ganzhou ,China

ygliang30@126.com

Li Zhang Huan li School of Electrical Engineering & Automation

Jiangxi University of Science and Technology, JXUST Ganzhou ,China

Zhangli2009200@163.com

Abstract—In this thesis, we propose a skin color detection algorithm based on adaptive model and design Gaussian classifier. Confirm face according to the geometric relationship of face features and organs. To raise the detection rate, we use the mosaic rules to make a further verification. Experimental results show that the method we put forward can detect the facial area correctly.

Keywords- face detection, face location, adaptive skin color model, Gaussian model

I. INTRODUCTION It’s a key point to distinguish face from other things in

color images. Skin color detection depends on facial details as well as face pose, expression changing and so on which remains robustness and stability. It becomes s natural idea to detect face with skin color data in color images. Many scholars put forward up the facial detection based on skin color model in different color spaces. Among the models, the extraction of pixel is influenced by hue while the brightness also makes the effect. And based on this situation, it’s necessary to build the models with several color spaces. Terrillon [1] evaluates nine color spaces and skin color models finding that it’s better to build a mixed Gaussian model to show the skin color clustering than a single model. So the domestic and foreign scholars proposed a few algorithms based on different skin color models and the achievements were good [2,3,4].

In this paper, we propose a adaptive skin color model by YCbCr space. The model would automatically divide the skin color based on brightness data. Then candidates faces are found out by calculating the divided skin color area feature parameters. Finally, using the mosaic rules to make the face detection and raising the accurate rate.

II. ADAPTIVE SKIN COLOR MODEL The flow chart of the adaptive skin color model is shown in

Figure.

A. Statistics of skin color pixel We use the database from M. Jones [5]. Firstly, we

transform the color images which contain the skin color from the RGB space into the YCbCr space. Since the skin color in training database is marked by masks, we count the parts which

are marked and rank every pixel by an ascending sort of brightness Y. Divide the weight Y into N parts and count the corresponding chroma (Cb, Cr) mean value im and

covariance iC .

Figure 1. The flow chart of adaptive skin color model

B. Discrete skin color model In the paper, the Gaussian model is designed and the skin

color similarity is calculated by the formula (1):

))()(5.0exp(),( 1 mxCmxCrCbP T −−−= − (1)

where m is mean value, )(xEm = , C is covariance, }))({( TmxmxEC −−= and

Tcrcbx ),(= . We can get N mean values and covariances corresponded to

the different brightness Y since we split the brightness data into N parts. After getting N skin color models ( , )P Cb Cr , we connect them with brightness data Y and choose the model parameter ( , )m C based on Y.

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C. The calculation of adaptive skin color model parameters The model parameters is different because the distribution

of skin color is influenced by brightness so it’s necessary to build a mapping between brightness and model parameters. We make the mapping between Y and model parameters by BP neural network which choose the models we need. Firstly, split the skin color data into N parts based on Y and get the every chromatic value (Cb, Cr) mean im and covariance iC then

take quantification to make the No. i corresponding to iY . So

we can get N training samples{ }, ( , )i i iY m C , 1, 2, ,i N= .

The Y is input vector and the im and iC is the output target vector of neural network. The input vectors of the NN are Y and its hidden layer nodes is adjusted by the experiment results. NN output layer contains mean values and covariance.

Training samples are normalized and used for the training NN. Gradient descent algorithm is used to adjust the weights. For some unknown pixel points, BP neural network outputs skin color model parameters based on brightness Y to make the self-adjusting of the model.

III. SKIN COLOR SIMILARITY CALCULATION To detect a new image, We transform the image from the

RGB color space into the YCbCr and make the brightness Y(i) as input signal for BP neural network. And get the

corresponding Gaussian model mean value ( im ) and

covariance ( iC ) so is the Gaussian model:

))()(5.0exp()( 1iii

Tiii mxCmxxP −−−= − (2)

Get the skin color probability value from the chromatic value ( , )T

i ix Cb Cr= . Then every skin color probability value divided by maximum probability value, the corresponding gray value which means the similarity of skin color is get.

IV. SKIN COLOR SEGMENTATION The skin color similarity image is segmented by OTSU. We

can find some probable isolated skin color point and parts which are caused by similar color pixel. To eliminate the effects, most of them will be wiped by median filter and mathematical morphology filter and some small parts would still reserve because of the illumination condition changing and complex background .

V. FACIAL GEOMETRIC FEATURES CALCULATION To wipe off non-faces parts like hands, and neck, we take a

further check of the skin color parts with sharp information and extract face. The flow chart of searching face is shown in Figure.2.

Image data

Skin color segmentation

Connected region mark

candidates face area

Mosaic verification

Length-width ratio Region area Euler number Regional

central

Figure.2 .The flow chart of face candidate selection

A. Euler numbers Each Euler numbers means the number of cavities in each

part. There are black cavities displayed in eyes, noses and mouth by the above treatment. Count the Euler numbers and take the threshold as 0. If ruler numbers is more than 0, we take this part as face .For author/s of only one affiliation (Heading 3): To change the default, adjust the template as follows.

B. Length-width ratio of the region and the facial boundary rectangle

Re _Re _

ct wratio

ct h= (3)

Where Rect_w is the width of boundary rectangle and

Rect_h is the height, set a threshold R, R ],[ 21 nn⊂ , if ratio R⊂ , it’s facial part or it’s not.

1 , ( , )( , )

0 ,x y object

I x yother

∈= (4)

C. Region area For binary image I(x,y),Area A is its zeroth-order moment:

Α===

ight

0y

idth

0x

),(IHW

yx (5)

D. Regional central Face should be a convex polygon and its center is in it, the

formula is:

1 1 1 1

1 1( , ) ( , )L W L W

y x y x

x xB x y y yB x yA A

− −

= = = =

= = (6)

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Where A is the region area. After getting the center , compare it with x and y which are got from border statistics, if it’s beyond, it’s non-faces.

VI . FACIAL VERIFICATION BASED ON MOSAIC

To reduce the false rate, we take a further check for faces with mosaic rules. The mosaic rules of the eyes, nose, mouth and so on are:

>>>>>>

)9()8(&)7()8()6()3(&)2()3()4()1(&)2()1(

TTTTTTTT

TTTT

(7)

Where T(i) is defined gradient along direction x. Rely on the gradient relationship of x, keep the detection result when it meet formula (7) or leave it.

VII. EXPERIMENTAL RESULTS AND ANALYSIS

A..Identify the Headings After the color space transformation, every pixel brightness

data is taken as input signal of neural network and get the model which is used to judge whether it is skin color. Using the skin color segmentation algorithm the result is shown in Fig.3. First column is original image, second column is skin color probability image and the third column is segmentation results in Fig.3. From result we can find that most non-faces parts are wiped out.

B. Facial detection results Calculate the four skin color area shape parameters based

on the above result and filter the suspected face by the mosaic rules and the result is shown in Figure.4. The red frame is with out mosaic which contain most parts of the face including neck and those in blue frame is with mosaic which wiped the neck and ear effectively.

Figure 4. Facial detection results

VIII. CONCLUSION A adaptive skin color model is proposed to extract the skin

color. Take the preliminary location based on facial features and its geometric relations then wipe out the fake part and finally detect the face with the mosaic rules.

REFERENCE

[1] J.C.Terrillon, M.N.Shirazei, H.Fukamach, and S. Akmatsu. Comparative performance of diffrent skin chrominance models and chrominance spaces for automatic detection of human faces in color images[J],IEEE Proceeding of the International Conference on Face and Gesture Recogniton,2000,41(4):54-61

[2] Xu Qingshi, Yue Xiang. Method of face detection based on improved YUV color space[J],Computer Engineering and Applications .2008, 44(34): 158-162

[3] Lamiaa Mostafa and Sherif Abdelazeem,Face Detection based on Skin Color Using Neural Networks GVIP Special Issue on Face Recognition 2007,23(6): 8-15

[4] Yang Guoliang,Huang Chaozhi,Ren Jinxia and Li Huan. Research on a Skin Color Detection Algorithm Based on Hybrid Color Model. 2010 3rd International Congress on Image and Signal Processing. Yantai,China,2010.10:1009-1013

[5] M Jones,J Rehg . Statistical color models with application to skin detection. nternational Journal of Computer Vision [J] 2002.1,81-96.

Figure 3. Skin detection results

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