analysis of human skin modeling and...

International Journal of Research in Science and Technology http://www.ijrst.com

(IJRST) 2016, Vol. No. 6, Issue No. IV, Oct-Dec e-ISSN: 2249-0604, p-ISSN: 2454-180X

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INTERNATIONAL JOURNAL OF RESEARCH IN SCIENCE AND TECHNOLOGY

ANALYSIS OF HUMAN SKIN MODELING AND

CLASSIFICATION

HITESH KAPIL1, VIKAS CHAWLA

2

1Department Of Electronics and Communication Engineering , GGGI, Shahabad

2Assist. Professor, Department Of Electronics and Communication Engineering , GGGI, Shahabad

Haryana , Shahabad

ABSTRACT

Human face perception is an extensive research that mixed up in computer vision community. Human face

localization and detection is frequently the action that is first applications such as for instance movie clip

surveillance, individual computer screen, facerecognition and image database administration. Locating

andmonitoring faces that are human being a prerequisite for face recognition and/or facial expressions

analysis, though it is oftenthought that a face that is normalized is available. For detecting face there are

various algorithms skin that is including based algorithms. Color is anfeature that is extremely important of

faces. Using skin-color as afeature for monitoring a real face has advantages being a few.

IndexTerms—Human Skin Modeling,Color Spaces, Skin Color Classification, Feature Extraction.

INTRODUCTION

“Biometrics encompass of technologies that prop automatic identification or verification of

individuality established on behavioral or physical properties”. Biometrics validates originality by

computing exceptional individual characteristics. The most main spans of biometrics involve

fingerprints, eyes and facial characteristics, hand geometry, retina, voice and touch. They have been

consigned to infrequent use in films and in a little elevated - protection power or martial equipment.

Nowadays, Biometrics is rising it’s requests in countless outlooks of area and confidential life. For

example, In the computer industry the most standard confidential identification numbers (PIN) and

passwords are being substituted by Biometrics. Even though these are yet the most public verification

and identification methods.

Figure 1Basic Image of Vision Detection Image Processing



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SKIN DETECTION

Skin detection [1] plays an vital act in a expansive scope of picture processing requests fluctuating

from face detection, face pursuing, gesture scutiny, and content-based picture retrieval arrangements

and to assorted human computer contact domains. Recently, skin detection methodologies established

on skin-color data as a cue has obtained far attention as skin color provides computationally competent

yet, robust data opposing rotations, scaling and partial occlusions. Skin color can additionally be

utilized as complimentary data to supplementary features such as form and geometry and can be

utilized to craft precise face detection systems. Skin color detection is frequently utilized as a

preliminary pace in face credit, face pursuing and CBIR systems. Skin-color data can be believed a

extremely competent instrument for identifying/ categorizing facial spans endowed that the underlying

skin-color pixels can be embodied, modeled and categorized precisely.

Figure 1 Denotes the Generic process of Skin detection

Most continuing skin detection algorithms [2] work well in a normal nature, but are not reliable in the

case of unpredictable and drastically changing real-world environments.

Skin detection process

The Skin detection process is broadly classified into six parts. They are,

1. Input image which is acquired from the digital camera.

2. Skin detection process using Color model.

3. Morphological operation such as erosion, dilation etc.,

4. Mask Creation to create the binary mask

5. Image cropping is to crop the exact portion of a Skin and

6. The output image contains the part of a Skin region.

Human Skin Detection

Since skin detection is gave in the RGB color space [3], Skin Detection Laws are extremely far subject

to the impact of illumination. A little color spaces have a property of separating the luminance

constituent from chromatic constituent and alongside that at least partial autonomy of chromaticity and

luminance is achieved. Such color spaces are for example YUV, RGB, HSI, TSL, etc. this implies the

algorithms find skin color agents in the 2D chromatic space, whereas color is delineated by merely two

dimensions and the third dimension is flouted.

When the impact of non-standard illumination from pictures is removed, the sound affects the quality

of reconstruction. The number of sound is increased in pictures alongside mild illumination, that way



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that dark pictures encompass extra sound than pictures seized below brilliant illumination conditions.

The number of sound affects the quality of reconstruction aftermath because loud colors are

transformed to wrong colors. If in precise case a loud skin span is transformed in delineated style, it

has an impact on the compactness of the reconstructed area. Hence the skin span segmentation gave by

face detection algorithm can be critically harmed. The 2D skin color determination could diminish the

impact of delineated kind of sound.

Human Skin Color Models

As delineated preceding colors frolic an vital act for object detection, pursuing and recognition.

Disparate color spaces have been counseled for skin established face detection such as RGB,

normalized RGB, HSV, and YCbCr. Usually HSV and YCbCr color spaces are helped to reclaim from

the intensity variations. A little of the preceding works are encompassed like, RGB, HSV, YUV color

spaces are utilized in the request of face credit and they become consequence as the complementary

data and the accuracy of face credit is altered by the color space [3].

HSV Color Model:

The setback of RGB does not furnish the correct data concerning skin color due to the setback of

luminance effects. HSV provides color data as Hue, Saturation and intensity of the Value. Hue

mentions to the color of red, blue and yellow and has the scope of 0 to 360. Saturation way purity of

the color and seizes the worth from 0 to 100%. Worth mentions the brightness of the color and

provides the achromatic believed of the color. From this color space, H and S will furnish the vital data

concerning the skin color. The skin color pixel ought to gratify the pursuing condition.

0 <= H <= 0.25; 0.15 <= S <= 0.9

YCbCr Color Model:

YPbPr and YCbCr are have the alike color constituents like Luminance, blue minus Luminance, red

minus Luminance but the YPbPr is the analog edition whereas as YCbCr is the digital version. It has

extra supremacy than the RGB & HSV ideal and extracts the skin serving of an picture employing

chrominance values. The skin serving of an picture ought to gratify as follows 140 <= Cr <= 165; 140

<= Cb<= 195; Even nevertheless it is a best way, due to a little reasons it gives low accuracy. So we

amass four benefits from the two color space as H, S, Cb, and Cr and whether these benefits are

gratifies the above conditions next the skin color segment being removed from the image. Frank flow

of Face Detection:

BI-Gaussian Skin Detection Model

The selection of a color ideal to embody human skin color is vital for face detection in a color image.

The YCbCr color space is suitable for real period request and is utilized in countless picture and video

standards. But in order to display the efficiency of the counseled way, the YCbCr space is utilized

instead of normalized color spaces like r-g and NCb-NCr [3].



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The distribution of skin color can be represented by a Gaussian model G (m, C), with the mean

where x = (Cb, Cr)

T Cr is the chrominance vector, and the covariance

Selecting training pixels from manually segmented skin spans can admission the earth truth skin ideal

parameters. But the ideal parameters could vary for individually selecting training data. Below

changing illumination, the color of skin pixels have additionally modified and distributed beyond of

the skin color cluster according to the normal skin color ideal, and the standard skin detection

algorithms can be precarious.

If selecting different skin color pixels below those conditions into the training set after accessing the

allocation ideal parameters, the skin ideal is trained and adapted to accord the skin pixels below those

conditions as skin. But it will weaken the skin color clustering result in the chromatic space and make

the presentation of skin detection algorithms inferior, because countless non-skin pixels are incorrectly

categorized as skin pixels. Consequently, a powerfully clustering skin color ideal leads to an rising

skin fake rejection and a cutting skin fake alarm. On the contrary, a weekly clustering skin color ideal

leads to a cutting skin fake rejection and an rising skin fake alarm. That is to say, there are constraint

relations amid the degree of skin color clustering and the presentation of the skin detection algorithm.

A better clustering skin model can be evaluated as that having both smaller skin errors and meanwhile

having a higher correct skin decision.

Figure 2Bi-gaussian skin detection model (left) and its projection on chrominance plane (right)

[3]

Classifier Based Approachs

A association algorithm for a human skin classifier that is able to efficiently categorize skin color tones

in each given color space, skin color occupies a portion of such a space, that could be a compact or

colossal span in the space. Such span is normally shouted the skin color cluster. A skin classifier is a

one-class or two-class association problem. A given pixel is categorized and labeled whether it is a

skin or a non-skin given a ideal of the skin color cluster in a given color space. In the context of skin

association, real positives are skin pixels that the classifier accurately labels as skin.



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True negatives are non-skin pixels that the classifier accurately labels as non-skin. Each classifier

makes errors: it can wrongly label a non-skin pixel as skin or a skin pixel as a non-skin. The preceding

kind of errors is denoted to as fake positives as the afterward is fake negatives. A good classifier ought

to have low fake affirmative and fake negative rates. As in each association setback, there is a tradeoff

amid fake positives and fake negatives. The extra loose the class frontier, the less the fake negatives

and the extra the fake positives. The tighter the class frontier the extra the fake negatives and the less

the fake positives are received. The alike applies to skin detection [5].

PROPOSED WORK

Human skin color classification finds out to which color tone the skin belongs. The simplest and most

employed technique for skin modeling is to explicitly define skin region. The advantage of this method

is the simplicity of detection rules which leads to building a very fast classifier. Other skin modeling

techniques employing statistical based approaches are involved such as neural networks, kmeans

clustering [6] and Bayesian networks [7].

However, skin modeling is complex and quite challenging. In fact, skin color in an image depends

mostly on illumination conditions which affect the distribution model of the skin color. Other

problems facing skin color classification are shade and shadow occlusions, resolution as well as skin

tone variation between races. The purpose of this study is to develop a skin color classifier into skin

color tones in order to improve the face recognition results based on Finding principal components

and vectors in the input signal.

In statisticalterms, we wish to find the principal components of the distribution of skin and covariance

matrix of the set of images; treating an image as a point in a very high dimensional space. The

principal components are ordered, each one accounting for a different amount of the variation among

the face images. These principal componentscan be thought of as a set of features that together

characterize the variation between images. Each image location contributes more or less to each

eigenvector, so that we can display the principal componentsas a Model.

The main purpose of finding principal components for Skin analysis is the analysis of data to identify

patterns and finding patterns to reduce the dimensions of the dataset with minimal loss of information.

The desired outcome of the Finding principal components analysis is to project a Skin feature space

onto a smaller subspace that represents Skin model properly. Finding Skin principal components can

help to classify skin patterns. By reducing the computational costs and the error of parameter

estimation by reducing the number of dimensions of our feature space by extracting a subspace that

describes our skin model most efficiently.

RESEARCH OBJECTIVE

The process of skin detection involves four stages pre-processing, edge detection, feature extraction,

Skin detection. Following objectives of both Training and Test are chosen for this research work:



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Training Phase

1. To acquire an initial set of Skin images as the training set.

2. To calculate the principal components from the training set, keeping only images that

correspond to the highest principal components. These images define the skin space. As new

images are experienced, the principal components can be updated and recalculated.

3. To calculate the corresponding distribution in weight space for each known individual, by

projecting skin images.

Having initialized the system, the following steps are then used to recognize new face images during

the Test Phase

1. To calculate a set of weights based on the input image and the principal components by

projecting the input image onto each of the principal components.

2. To determine if the image has skin at all by checking to see if the image is sufficiently close to

weights defended.

3. To check if it has a skin, classify the weight pattern as either a known or as unknown.

4. Extract the skin boundary for further analysis

Human skin color

Human skin color ranges in variety from the darkest brown to the lightest pinkish-white hues. Human

skin pigmentation is the result of natural selection. Skin pigmentation in human beings evolved

primarily to regulate the amount of ultraviolet radiation penetrating the skin, controlling its

biochemical effects.

The actual skin color of different humans is affected by many substances, although the single most

important substance is the pigment melanin. Melanin is produced within the skin in cells called

melanocytes and it is the main determinant of the skin color of darker-skinned humans. The skin color

of people with light skin is determined mainly by the bluish-white connective tissue under the dermis

and by the hemoglobin circulating in the veins of the dermis. The red color underlying the skin

becomes more visible, especially in the face, when, as consequence of physical exercise or the

stimulation of the nervous system (anger, fear), arterioles dilate.

There is a direct correlation between the geographic distribution of UV radiation (UVR) and the

distribution of indigenous skin pigmentation around the world. Areas that receive higher amounts of

UVR, generally located closer to the equator, tend to have darker-skinned populations

Figure 3 Human Skin Variation throughout the work



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Pixel Extraction

and Filtering

Skin Color

Modeling

Color Model

Classifier

Training

Trained Model

Classified Skin

Regions

Image

Segmentation

Skin Segmented

Image

RESULTS AND ANALYSIS

Recognition of human skin is an important task for both computer system vision and illustrations. For

computer vision, accurate recognition of epidermis surface can greatly assist algorithms for human

face recognition or facial feature tracking. Many skin segmentation methods depend on skin color

which have actually numerous problems. The pores and skin will depend on human race and on

lighting conditions, although this can be averted in some methods utilizing YCbCr color spaces in

which the 2 components Cb and Cr depend only on chrominance, there still many problems with this

method because there are many things into the genuine world that have a chrominance in the range of

the human skin which may be incorrectly thought to be skin. So we have used Skin Gamot of L*A*B*

color space for classification.

Figure 4 Multiethnic Image taken for Segmentation



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Figure 5The image in Color Spaces including RGB, HSV, YCRCb, L*a*b*

From above picture it is clear that HSV color plane works good for segmenting images, and figure

below shows the poorly segmented image using MATLAB’s image Thresholder. Following Code for

thresholding was used.

channel2Min = 0.232;

channel2Max = 0.612;

% Define thresholds for channel 3 based on histogram settings

channel3Min = 0.546;

channel3Max = 0.950;

Skin Model Generation

The Skin Data was taken from a spectrophotometer from various human individuals.

The mean C value = 22.3.

The mean H value = 55.0.

The mean L value = 61.0.

The mean A value = 22.3.

The mean B value = 0.4.

Segmented Skin in HSV Plane

200 400 600 800 1000 1200 1400 1600 1800 2000

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400

600

800

1000

1200

1400



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Few Points can be noted from the 3D Skin Model

1) Note how the shape of the gamut is like a banana or boomerang, This is because Very pale (white)

individuals will have low chroma (saturation) values because white does not have much color.

Similarly very dark individuals have low chroma (saturation) values because black does not have

much color. As the skin lightness gets into the medium range, then you will have more colorful

skin tones.

2) Also the Hue (the angle between the a and b axis) tends to not have very much spread to it. The

hue is determined by the intrinsic properties of the skin such as the chemical composition of

hemoglobin and melanin.

3) The vast majority of the skin color differences can be attributed to changes in the Lightness Values

(vertical location) or the Chroma values (radial distance).

4) From this scatterplot, we can see how trying to segment out skin color by carving out a box

(thresholding) in LAB color space, or LCH color space (the cylindrical representation of LAB

space) would not be very robust since you would be including non-skin colors in the interior of the

"bow" of the boomerang/banana shape.

5) From the shape of the scatterplot, you can see how a lookup table would be a more effective way

of detecting skin color. Alternatively if you can get an analytical formula to describe the

"envelope" of the banana-shaped gamut, then you could use that.

1520

25

0.2

0.4

40

50

60

70

B Values

The human skin 3D color gamut.

Skin L*A*B* values.

A Values

L V

alu

es



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Figure 6:Skin likelihood of each pixel in the input image

Figure 7Segmented Skin Using the proposed Model

CONCLUSION

The everyday life of humans in higher developed countries is more and more surrounded by computers

assisting us in all kinds of activities. It is becoming ubiquitous. An important facet in achieving

ubiquity is the supply of more user-friendly, intuitive, and efficient personal computer system

relationship (HCI), i.e., migrating from traditional keyboard and mice inputs towards unobtrusive user

interfaces that tend to be natural to humans and that the consumer could even not be alert to, e.g.,

speech input, face recognition, facial expression interpretation, hand gesture recognition, and large-

scale human anatomy language. In this context human skin colour may be an important visual feature.

Skin is a complex landscape that is hard to model for many reasons. The skin texture features is

determined by numerous variables such as human anatomy location (knuckle vs. torso), subject

variables (age/gender/health) and imaging parameters (lighting and camera). Also much like numerous

real world surfaces, epidermis appearance is highly suffering from the direction from where it is seen

Skin likelihood of each pixel: Min=-15.79, Max=6.52

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Likelihood thresholded at zero

200 400 600 800 1000 1200 1400 1600 1800 2000

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and illuminated. Skin recognition is employed in many applications ranging from algorithms for face

recognition, hand gesture evaluation, and to objectionable image filtering. In this work a skin

recognition system was developed and tested. While many skin segmentation algorithms relay on skin

shade, our work relies on both skin tone and texture features to give a much better and much more

efficient recognition accuracy of skin textures. We used Look up tables and skin gamut (model) of

more than 582 individual to classify input designs images to be epidermis or non skin designs. The

system gave very encouraging results during the generalization of skin.

FUTURE SCOPE

Recognition of human skin is an important task for both computer system vision and illustrations. For

computer vision, accurate recognition of epidermis surface can greatly assist algorithms for human

face recognition or facial feature tracking. Many skin segmentation methods depend on skin color

which have actually numerous problems. Most of the skin detection techniques use a preprocessor for

skin detection. Though skin color analysis often produces good results, but fails when the image

contains cluttered background and shadows. To improve the accuracy of the classifier, various other

features such as shape, spatial and motion information can be used along with skin-color information.

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