skin-based face detection: an implementation 2011/lec5.… · skin-based face detection: an...

1/27/2011 ECE 523: Introduction to Biometrics 1

Skin-based Face Detection: An Implementation


Outline

• Face Detection Framework

• Live Class Example


Generic Framework for Face Detection

Skin

Detection

Morphological

Processing

Template

Matching

Face

Selection


Methods of Skin Detection

• Pixel-Based Methods

– Classify each pixel as skin or non-skin individually, independently from its neighbors.

– Color Based Methods fall in this category

• Region Based Methods

– Try to take the spatial arrangement of skin pixels into account during the detection stage to enhance the methods performance.

– Additional knowledge (in terms of texture … etc) are required


Different Color Models

• RGB

• Normalized RGB

• HIS, HSV, HSL

• TSL

• YCrCb

• Perceptually uniform colors

• Others


Bayes Decision Rule

• N-class case

Given a feature vector x, assign it to class wj if:

• Likelihood Ratio: 2-class case

Likelihood ratio Threshold

An Example1

1 Ciarán Ó Conaire, Computer Vision Source Code, Skin Detection, http://clickdamage.com/sourcecode/index.html

fn = 'IMG_1762.jpg';

im = double(imread(fn));


An Example

• Non-parametric histogram-based models were trained using manually annotated skin and non-skin pixels.

• A total of 14,985,845 skin pixels and 304,844,751 non-skin pixels were used.

• Created an RGB histogram for "skin pixels" and another one for "non-skin pixels" (they

are 32x32x32 in size)

• For a particular bin (i.e. pixel color), the log likelihood of it being skin is

where H refers to the skin histogram and h refers to non-skin histogram.


An Example

% Compute the skin likelihood for each pixel

skinprob = computeSkinProbability(im);

function skinprob = computeSkinProbability(im)

persistent smodel;

if (isempty(smodel))

% load skin model

skinmodfn = 'skinmodel.bin';

fid = fopen(skinmodfn, 'rb');

tmp = fread(fid, inf, 'real*4');

fclose(fid);

K = 32;

smodel = zeros(K,K,K);

smodel(:) = tmp(:);

clear tmp

end

Part I


An Example

if (size(im,3) ~= 3)

warning('Input image does not have 3 bands. RGB image

required.');

skinprob = [];

return

end

im = double(im);

im2 = 1 +

floor(im(:,:,1)/8)+floor(im(:,:,2)/8)*32+floor(im(:,:,3)/8)*

32*32; % 32 bins

skinprob = smodel(im2);

function skinprob = computeSkinProbability(im)

… Part II


An Example

% compute and display a binary skin map using a threshold of 0

figure, image((skinprob>0)*64);

colormap('gray');

title('Likelihood thresholded at zero');

Likelihood thresholded at zero

50 100 150 200 250 300 350 400 450 500 550

50

100

150

200

250

300

350


Live Class Example

(a) Divide data into training and testing sets

(b) Work on training data – skin and non-skin (c) RGB -> YCbCr (important to LP filter Cb, Cr to remove noise) (d) Scatter plot two classes (e) Get 2D mean, covariance for both skin/non-skin (f) Apply Bayes classifier (with equal priors) to testing set (g) Apply Bayes classifier with priors trained from training data


Skin Detection Results

(Input) (Output)

1 – Detected skin pixels (white) 0 – Non-skin (black)


Generic Framework for Face Detection

Skin

Detection

Morphological

Processing

Template

Matching

Face

Selection


Morphological Processing

• Morphological closing – closing fills up any narrow black regions in the image

I = double(I);

filledBW = imfill(I,'holes');


Morphological Processing • Morphological erosion – removes pixels on object boundaries

se2 = strel('disk',10); % Structuring element

erodedBW = imerode(filledBW,se2);


Morphological Processing • Morphological dilation – adds pixels to the boundaries of objects in an image

se1 = strel('disk',8);

dilateBW = imdilate(erodedBW,se1);



• Dilated binary image is multiplied with binary image from the segmentation process to maintain the holes.

x

dilateBW = immultiply(dilateBW,I);



• Region labeling – each clustered group of pixels can be identified at a single region; each region can be analyzed further if it’s a face or not

[labelBW,num] = bwlabel(dilateBW,8);

color_regions= label2rgb(labelBW, 'hsv', 'black', 'shuffle');

figure, imshow(color_regions)



• Euler number – the total number of objects in the image minus the number of holes in those objects

• Consider only objects with at least one hole

eulerBW = euler_test(labelBW);



• Aspect ratio test – any region with aspect ratio unlikely to be a face is rejected

aspectBW = aspect_test(eulerBW)


Template Matching • width, height, orientation and centroid of binary region under consideration has to

be computed using regionprops()

• template face image is resized, rotated and its centroid placed on the centroid of the region in original grayscale image with only one region

Template

original grayscale image with only one region

Rotated template

• crosscorrelation is calculated, threshold of 0.6 is set by experiment and the regions which has less crosscorrelation value is rejected.


Final Detection

Template Matching

Final Detected Face


Summary


References

Ciarán Ó Conaire, Computer Vision Source Code, Skin Detection, http://clickdamage.com/sourcecode/index.html Jie Yang and Alex Waibel, "A Real-Time Face Tracker", CMU CS Technical Report. J. Cai & A. Goshtasby & C. Yu, "Detecting Human Faces in Color Images", Wright State University, U. of Illinois. G. Wyszecki and W.S. Styles. Color Science: Concepts and Methods, Quantitative Data and Formulae, second edition, John Wiley & Sons, New York 1982. Y. Gong and M. Sakauchi, "Detection of regions matching specified chromatic features", Computer Vision and Image Understanding, vol. 61, no. 2, 1995, pp 263 – 269 R. Ramesh, Kasturi R. and Schunck B., Machine Vision, pp 31 - 51, McGraw Hill, New York 1995. Henry Chang and Ulises Robles, Face Detection, http://www-cs-students.stanford.edu/~robles/ee368/source.html

http://clickdamage.com/sourcecode/index.html

http://clickdamage.com/sourcecode/index.html

http://www-cs-students.stanford.edu/~robles/ee368/source.html






skin-based face detection: an implementation 2011/lec5.… · skin-based face detection: an...

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