Skin Detection using Support Vectors

Transcription

1 Term Project Digital Image Processing Skin Detection using Support Vectors Group Members: Ankit Kumar Shrivastava : Y3051 Ankit Misra : Y3053 Gaurav Teltia : Y3120 Kuldeep Singh : Y3155 Nitin : Y3206 Saurabh Khanna : Y3321

2 Need for Skin Detection There have been a lot of changes in the ways humans interact with computers. Modern Human-Computer applications Speech synthesizers Hand gesture recognition Biometric identification In all the above application, skin color is often used as a cue for detecting, localizing and tracking targets containing skin, like faces and hands in an image

3 Human Skin The color of human skin is created by a combination of blood (red) and melanin (yellow, brown). Skin colors lie between these two extreme hues and are somewhat saturated. The human skin is a fraction of the actual color cube, about 0.25 % of the total colors Except for extremely hairy subjects, which are rare, skin has only lowamplitude texture.

4 Human Skin Robust skin segmentation involves the formulation of an efficient mathematical model to represent the skin color distribution. Segmenting skin from real-world images is a difficult task even though human skin is known to possess a unique color range, which is but a fraction of all the possible color combinations.

5 Human Skin Color Model In HSV space

6 Neural Network Based Color Model for Skin Detection The training of the skin color model based on neural networks is composed of three stages : Collection of skin color Interpolation Skin color classification. This scheme proposes to model skin color, by training a three-layered network with skin and non-skin examples, using the back propagation algorithm. The trained network is used to estimate the probable skin regions in a three-dimensional color cube and interpolate to provide a reasonable estimate of the skin color distribution Since the skin color samples we have collected do not represent the skin color population, we need to interpolate for the skin color that we don t have using the skin color we have.

7 Neural Network Based Color Model for Skin Detection Multilayer perceptron trained using the back-propagation learning algorithm is used for skin color interpolation. Figures below illustrates the back propagation minimize the mean square error between the desired output and the actual output. The back propagation algorithm uses supervised learning where the training of the network is done with known input and output data. Once the network is trained, its weights can be used to compute outputs for new input values. Multilayer perceptron Back-Propogation

8 Neural Network Based Color Model for Skin Detection Applications: Virtual Studios To detect and track human faces in real-time video sequences In face and gesture recognition systems to identify and index individuals for surveillance purposes.

9 Neural Network Based Color Model for Skin Detection Advantages: The proposed method extracts skin very well irrespective of color of the skin It is able to detect faces in varying lighting environments too. The method does not need to scan the entire image every time. The skin detector reduces the search area considerably, saving a lot of computation time and enhancing the speed of the system.

10 Neural Network Based Color Model for Skin Detection Limitations: Training a neural network for the skin color detection is challenging because of the difficulty in characterizing non-skin color. Typically a huge database is required to train the network which is both expensive and computationally cumbersome. Minimizing overfitting requires a great deal of computational effort.

11 RGB Model Based Skin Detection Distribution of skin-color of different people is clustered in a small area of chromatic space and can be represented by a Gaussian model Skin color model should be adaptable for any skin color under any lighting conditions. RGB representation of color images is not suitable for characterizing skin-color as it also represents luminance. Luminance may vary across a person's face due to the ambient lighting and is not a reliable measure in separating skin from non-skin region. Luminance can be removed from the color representation in the chromatic color space.

12 Skin Detection Model We collected two sets of 15 color images each with human faces from the World Wide Web. First set are images of people with white coloured skin (Caucasian and a part of Asian race), second set of people with brown and black coloured skin (African and a part of Asian race). Then manually selected little rectangle samples of skin from every image of each set were filtered using a low-pass filter to reduce the effect of noise. Normalized values of red and green color for each pixel of filtered samples are calculated A kind of normal statistical model that is estimated with parameters mean vector and covariance matrix:

13 Estimation of Skin Color Model Single Gaussian Model: Chromatic( normalized) colors r,g,b are defined as: r = R/(R+G+B) g = G/(R+G+B) Mean: m = E {x} where x = (r g)t Covariance: C = E {(x m)(x m)t} Skin likelihood of an input chrominance vector x is given by p(x) = exp [ -0.5 (x m )t*inverse)*( x-m)] So we could create three sets: one set of people with white skins Set W), second set of people with black-brown (Set B) skins and third set as an union of first and second set (Set WB).

14 Estimation of Skin Color Model Mixed Gaussian Model: We estimated the weights of each Gaussian model experimentally and we will calculate skin likelihood of an input chrominance vector x by formula : p(x) = 0.3NW(m,C) + 0.4NB(m,C) + 0.3NWB(m,C) where x = ( r g ), NB(m,C) is Gaussian model of Set W, NB(m,C) is Gaussian model of Set B, NWB(m,C) is Gaussian model of Set WB.

15 Processing an Image The first step in the processing of an input picture is creating a skinlikehood image in which each pixel corresponds to the probability of occurrence of skin-color (in the same pixel in the original input image). The values of probability can be easily transformed into greyscale values. Second step is creating a skin-segmented image by using a threshold value of probability. We can use fixed threshold value for every image or adaptive thresholding. A fixed threshold value is generally more efficient.

16 Advantages Skin regions can be made more integral by use of low pass filtering. This method can be effectively used in images of faces that have some parts more affected with ambient light. Quality of estimated color model can easily improved by using larger set of analysed samples. EM algorithm can be used to estimate more appropriate values of mixture Gaussian model.

17 Disadvantages To improve performance we use more number of samples but computation burden increases considerably. Algorithm works better for images of white skin than black ones.

18 Problem Statement What we need is a function which can tell whether a given color pixel is a skin pixel or not. Input Color Pixel (RGB/HSV) F(R,G,B) 0 OR 0 : Input pixel is not a skin pixel 1 : Input pixel is a skin pixel 1

19 Why to use Support Vector Classifier One of the easiest way to determine if a particular RGB color corresponds to skin color or not, we built a lookup table with all possible combinations of RGB. 16 million long database Large storage spaces and Computational times required. Who will build such a huge database?? R G B 0=not skin pixel 1= skin pixel : : : 0 or 1 : : : 0 or 1 : : : : : : : :

20 Why to use Support Vector Classifier A Smart way is evaluate a non-linear function that maps all the RGB space to two sets.( One for skin pixels and the other for non-skin pixels). Such a non-linear function will not have a closed form expression and hence annotated by several inequalities. The number of inequalities become large if we want to represent the function accurately.

21 Support Vector Machines We want to map the RGB space into a higher dimensional space such that the skin pixels and nonskin pixels are linearly separable.

22 Support Vector Machines Input Color Pixel (RGB/HSV) Map to Higher Dimensional space Training Set Linear Classifier 1 [ A skin pixel] 0 [ Not a skin pixel] Final Algorithm

23 DEMO

24 Thank You

25 1) Matlab Codes Appix Skin.m function skin2(fname,race) %reading the image img=imread(fname); %converting RGB image to HSV image hsv_img = rgb2hsv(img); %uploading the database data=importdata('data2.txt'); [m,n]=size(data); % x=data d=decision x=zeros(m,4); d=zeros(m,1); for i=1:m [H S V]=rgb2hsv(1/255*[data(i,1) data(i,2) data(i,3)]); x(i,1)=h; x(i,2)=s; x(i,3)=v; x(i,4)=data(i,4); d(i,1)=data(i,5); %uploading K matrix K=importdata('K.txt'); %a=lagrangian multiplier column vector a=importdata('a.txt'); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%detection of skin%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %displaying original image colormap(gray(256)); image(img); [q,p,r]=size(img); %final_img=zeros(q,p); for X=1:5:p for Y=1:5:q H=hsv_img(Y,X,1); S=hsv_img(Y,X,2); V=hsv_img(Y,X,3); out=0; if(race(1)==1) for i=1:m out=out+a(i)*d(i)*exp((-1/(2* ))*((x(i,1)-h)^2+(x(i,2)- S)^2+(x(i,3)-V)^2+(x(i,4)-1)^2));

26 if(out>0) % final_img(y,x)=255; rectangle('position',[x,y,2,2]); if(race(2)==1) out=0; for i=1:m out=out+a(i)*d(i)*exp((-1/(2* ))*((x(i,1)-h)^2+(x(i,2)- S)^2+(x(i,3)-V)^2+(x(i,4)-2)^2)); if(out>0) % final_img(y,x)=255; rectangle('position',[x,y,2,2]); if(race(3)==1) out=0; for i=1:m out=out+a(i)*d(i)*exp((-1/(2* ))*((x(i,1)-h)^2+(x(i,2)- S)^2+(x(i,3)-V)^2+(x(i,4)-3)^2)); if(out>0) % final_img(y,x)=255; rectangle('position',[x,y,2,2]); update_data.m function update_data %uploading the database data=importdata('data2.txt'); [m,n]=size(data); % x=data d=decision x=zeros(m,4); d=zeros(m,1); %coverting RGB data to HSV data for i=1:m [H S V]=rgb2hsv(1/255*[data(i,1) data(i,2) data(i,3)]); x(i,1)=h; x(i,2)=s; x(i,3)=v; x(i,4)=data(i,4); d(i,1)=data(i,5);

27 %mapping from input space to feature space %formation of K matrix K=zeros(m,m); for i=1:m for j=1:m K(i,j)=exp((-1/(2* ))*((x(i,1)-x(j,1))^2+(x(i,2)- x(j,2))^2+(x(i,3)-x(j,3))^2+(x(i,4)-x(j,4))^2)); %updating the K matrix dlmwrite('k.txt', K, 'delimiter', '\t', 'precision', 4); %a=lagrangian multiplier column vector a=zeros(m,1); %updating the lagrangian multipliers Kd=zeros(m,m); for i=1:m for j=1:m Kd(i,j)=K(i,j)*d(i)*d(j); a=inv(kd)*ones(m,1); dlmwrite('a.txt', a, 'delimiter', '\t', 'precision', 4);

28 2) Results: Before Before After After