HUMAN SKIN IDENTIFICATION: A REVIEW SONAM SAHU
M.tech Scholar (Communication System) Dept. Of Electronics & Communication Engg. GGITS Jabalpur
RAJ TIWARI
Asst.Prof. Dept. Of Electronics & Communication Engg. GGITS Jabalpur
ABSTRACT: Skin color and textures are important cues that people use consciously or unconsciously to infer variety of culture-related aspects about each other. Skin color and texture can be an indication of race, health, age, wealth, beauty, etc. However, such interpretations vary across cultures and across the history. In images and videos, skin color is an indication of the existence of humans in such media. Therefore, in last two decades extensive research have been done on skin detection in images.
Skin detection means detecting image pixels and regions that contain skin- tone color. Most the research in this area has focused on detecting skin pixels and regions based on their color. Very few approaches attempt to also use texture information to classify skin pixels. This paper, review the skin identification techniques.
Keywords: Skin detection, Bayes theorem, Gaussain model
1. INTRODUCTION
Human skin color information is an efficient tool for identifying facial areas and facial features, if the skin color model can be properly adapted for different lighting environments. Therefore, color information is convenient to use for face detection, localization and tracking, since it is invariant to rotation and robust to (partial) occlusion. There are some difficulties, mainly because different people have different
facial color, make-up, and individual variations.
For human color perception, a 3-D color space such as an RGB space is essential. Most video cameras use an RGB model; other color models can be easily converted to an RGB model. However, an RGB space is not necessarily essential for all other problems. Color segmentation can basically be performed using appropriate skin color thresholds.
2. RELATED WORKS
In [1] and [2] a mixture of Gaussians to model a skin color based on the observation that the color histogram for the skin of people with different ethnic background does not form a unimodal distribution, but rather a multimodal distribution is detailed.
Recently a comparative study of several widely used color spaces by modeling skin color distributions with either a single Gaussian or a Gaussian mixture density model in each space is presented [3].
Jones, M.J. [4], Burger, W [5] have demonstrated that skin color falls into a narrow band in the color space, and hence can be harnessed to detect pixels, which are of the color of human skin.
Through connectivity analysis and region growing, these skin color pixels can be grouped to give locations of face hypotheses. Chen and Wang [6, 7] have demonstrated successful results using a perceptual-uniform color space and a fuzzy logic classifier.
Dai and Albiol, A., [8] have also shown successful detection using a combination of color and texture information. Lee et al. [9] proposed
a quantized skin color regions merging by using color clustering, and filtering using approximations of the YCbCr and HSV skin color subspaces that are applied on the original image. A merging stage is then iteratively performed on the set of homogeneous skin color regions in the color-quantized image, in order to provide a set of potential face areas.
Automatic skin detection is a challenging task, especially under varying illumination and partial occlusions [1]. Another inherent difficulty is that skin tones can significantly vary across individuals. Several methods found in the literature are based on fixed thresholds [2], [3] and are sensitive to geometric variations of skin patterns and are not robust to image resolution changes.
An adaptive human skin detection method based on a normalized lookup table, whose resulting probability map is used to detect skin and non-skin regions in the images. Such regions are then refined through texture descriptors. Experiments are conducted to apply the methodology to several images and
results show the effectiveness of the method. Several methods for skin detection have been proposed in the literature [4]. They can be categorized as pixel-based and region-based approaches. Pixel- based methods classify each pixel as skin or non-skin without considering its neighbourhood, whereas region-based methods explore the spatial organization of neighbour skin pixels to improve the skin detection process.
Comprehensive surveys on skin color modelling and skin detection can be found in the literature [4]–
[6].
A skin color model is commonly used to identify if a pixelor region is skin or non-skin.
Various color spaces have been used in skin detection like RGB, HSV, YCbCr etc. Skin detection errors can also be reduced taking advantage of the fact that skin pixels are usually clustered in the spatial domain. Hence, various region-growth operations may help reject many false positives. Here, apart from the propagation technique, the seed extraction has a critical impact on the final outcome.
3. A FRAMEWORK FOR SKIN DETECTION
Skin detection process has two phases: a training phase and a detection phase. Training a skin detector involves three basic steps:
1. Collecting a database of skin patches from different images.
Such a database typically contains skin coloured patches from a variety of people under different illumination conditions.
2. Choosing a suitable color space.
3. Learning the parameters of a skin classifier.
Given a trained skin detector, identifying skin pixels in a given image or video frame involves:
1. Converting the image into the same color space that was used in the training phase.
2. Classifying each pixel using the skin classifier to either a skin or non-skin.
3. Typically post processing is needed using morphology to impose spatial homogeneity on the detected regions.
In any given color space, skin colour occupies a part of such a space, which might be a compactor large region in the space. Such region is usually called the skin
color cluster. A skin classifier is a one-class or two-class classification problem. A given pixel is classified and labelled whether it is a skin or a non-skin given a model of the skin color cluster in a given color space. In the context of skin classification, true positives are skin pixels that the classifier correctly labels as skin. True negatives are non-skin pixels that the classifier correctly labels as non-skin. Any classifier makes errors: it can wrongly label a non-skin pixel as skin or a skin pixel as a non-skin. The former type of errors is referred to as false positives (false detections) while the latter is false negatives. A good classifier should have low false positive and false negative rates.
As in any classification problem, there is a trade-off between false positives and false negatives. The more loose the class boundary, the less the false negatives and the more the false positives. The tighter the class boundary, the more the false negatives and the less the false positives. The same applies to skin detection. This makes the choice of the color space extremely important in skin
detection. The color needs to be represented in a color space where the skin class is most compact in order to be able to tightly model the skin class. The choice of the color space directly affects the kind of classifier that should be used.
4. SKIN COLOR REPRENSATION Color plays an important role for face detection. Different color spaces have been proposed for skin based face detection such as RGB, HSV, YCbCr[1]. A reliable skin model that is adaptable for different skin colors under different lighting conditions. RGB does not provide the right information about skin color because it not only contains r, g, b color values but luminance too.
Problem with HSV is pixels as large and small intensities are discarded. Luminance can’t be ignored as it may vary across a person’s face due to lighting. It is not reliable for separating skin and non-skin regions. So, this luminance should be removed from color images. This is done in chromatic color space. After removal of luminance, skin segmentation can be done
effectively. Though skin colors of different people vary over a wide range, they vary less in color than in brightness. To develop a skin color model, 15 skin samples of different ethnicities like Asian, American, and African etc are taken. Low pass filtering is done to remove the effect of noise .Then these distribution of skin colors of different people are clustered in chromatic color space and is represented by Gaussian Model N(m,n).
where:
Mean: μ=E{h}
Where h= [Cb, Cr]T
Covariance: cov(h1,h2)=E{(h1-μ1)(h2- μ2)} (1)
5. PROBABILITY OF DETERMINING SKIN REGION This step is performed which determines theprobability of each pixel belonging to skin when a skin color model is transformed from a color image to a gray scale image.
From the above Gaussian Fitted skin color model which determines skin color distribution of different people in a chromatic color space.
If a pixel having transformed from RGB color space to chromatic color
space has a chromatic pair value of(Cr, Cb), the likelihood of skin for this pixel can then be computed as follows.
P(Cr, Cb) = exp [-0.5(h - μ) )Tcov-
1(h2 – μ2)] (2) Where h = [Cr, Cb]T
All skin regions like eyes nose were shown brighter than the non-skin regions. With appropriate thresholding grey scale image is transformed to binary image which segregates skin regions from non- skin regions.
6. SKIN SEGMENTAION
Beginning with a color image first stage is to transform it to a skin likelihood image. This involves transforming every pixel from RGB to chroma representation and determining the likelihood value of pixel based on previous section.
The skin-likelihood image will be a gray scale image whose gray values indicate the likelihood of pixel belonging to skin. It may so happen that the detected region may not necessarily correspond to skin regions like dress, objects etc.
So, this stage is reliable in concluding that the detected regions have the same color as
that of the skin. Important point to be noted here is the non-skin regions acquired from this process are discarded and are not considered in face finding process.
To separate skin and non skin colored objects we go for thresholding techniques. Since people with different skin color have different likelihood, an adaptive thresholding process is required to achieve the optimal threshold value for each run.
Figure1:system overview.
6. HISTOGRAM EQUALIZATION The image histogram sometimes comprises for the most part dark
pixels; this is the situation of an inadequately exposed photograph . The image can be improved by steady addition however histogram equalization is normally more proficient method for this task.
Additionally, it can be applied if the contrast of the image is very small for any reason [13]. The purpose of the technique is to spread the histogram uniformly as much as possible over the full intensity scale. This is accomplished by estimating cumulative sums of the pixel samples for all gray level value x in the histogram. The number of gray levels is implied by the sum that should be allocated to the range [0, x], and it depends on the cumulative frequency t(x), and to the total number of gray levels g:
f x g t x ( ) ( )n
1 (3) Here n represents the total frequency, i.e. the number of pixels in the image.
7. NORMALIZED LOOKUP TABLE (LUT)
Several face detection and tracking algorithms [Chen et al. 1995], use a histogram based approach to
skin pixels segmentation. The color space (usually, the chrominance plane only) is quantized into a number of bins, each corresponding to particular range of color component value pairs (in 2D case) or triads (in 3D case).
These bins, forming a 2D or 3D histogram are referred to as the lookup table (LUT). Each bin stores the number of times this particular color occurred in the training skin images. After training, the histogram counts are normalized, converting histogram values to discrete probability distribution:
skin
skin c
P c
Norm (4)
where skin[c] gives the value of the histogram bin, corresponding to color vector c and Norm is the normalization coefficient (sum of all histogram bin values, or maximum bin value present ). The normalized values of the lookup table bins constitute the likelihood that the corresponding colors will correspond to skin.
This is also done by collecting measures of skin and non-skin pixel color samples and arranging them in a normalized histogram.
This histogram provides a
probability indicating how likely each pixel is skin or non-skin, such that a probability map is created to the entire image. With the application of a proper threshold, this map can be used to detect whether each pixel is skin or not. To make the detection process more adaptable and achieve better results, a measure of how homogeneous is the detected region is evaluated since human skin regions tend to be more homogeneous than other types of surfaces [15]. Thus, the achieved results are maintained in an assessment of how homogeneous every region is according to the probability map, that is, regions that are not considered homogeneous are discarded, whereas homogeneous regions are grown as long as they remain homogeneous and then used in the output. Finally, after considering two skin properties, color and homogeneity, the detection process is refined by taking texture information into account. Figure 1 shows the main stages proposed method.
Color histograms were constructed through both the skin
and non-skin groups of images.
RGB images were used to construct these histograms, each pixel forms a vector [RGB]which is translated to the lookup table as H([RGB]) = H(R + [G* 256] + [B * 256 * 256]) (5)
To find a probability of a pixel being in each group
H([RGB]) = [ )
∑ ) (6) For every test image, we use a threshold t and the LUT toclassify the image as follows.
If )
)≥tt then P is labeled as skin
where P(pixel | skin) is the probability of a pixel containing skin (informed by the histogram of the skin group) and P(pixel j: skin) is the probability of a pixel being in the non-skin group.
8. BAYES CLASSIFIER
The value of Pskin
c computed in (7) is actually a conditional probability P c skin
- a probability of observing color c, knowing that we see a skin pixel. A more appropriate measure for skin detection would be P skin c
- aprobability of observing skin, given
a concrete c color value. To compute this probability, the Bayes rule is used:
P c skin P skin P skin c
P c skin P skin P c skin P skin
(7)
P c skin andP c
skin
are directly computed from skin and non-skin color histograms (11). The prior probabilities P c skin
and
P c skin can also be estimated from the overall number of skin and non-skin samples in the training set [14]. An inequality
P c skin , where Θ is a threshold value, can be used as a skin detection rule [15]. Receiver operating characteristics (ROC) curve shows the relationship between correct detections and false detections for a classification rule as a function of the detection threshold. It turns out, that the ROC curve for P c skin
isinvariant to choice of prior probabilities, due to nature of the Bayes model. This means that P(skin) value affects only the choice of the threshold Θ.
If what is really needed is the comparison of P skin c
and
P skin c , not their exact values.
Using (11) the ratio of P skin c
and
P skin c , can be written as:
P skin c P c skin P skin P skin c P c skin P skin
(8)
Comparing (12) to a threshold produces the skin/non-skin decision rule. That after some manipulations, can be rewritten as:
1
P c skin P c skin
P skin K P skin
(9)
This shows, why the choice of prior probabilities does not affect the overall detector behavior - for any prior probability P(skin) it is possible to choose the appropriate value of K, that gives the same detection threshold Θ. It is also clear, that maximum likelihood (ML) and maximum a posteriori (MAP) Bayes classification rules compared in [15] are equivalent to (6) with different Θ values.
9. CONCLUSIONS
This paper presented a review of human skin detection method based on a probability map used to
detect skin and non-skin regions.
various steps in skin detection is detailed. A few notable methods are presented along-with mathematical formulation.
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