International Journal on Advanced Computer Theory and Engineering (IJACTE)
Analysis of Genetic Dominance through Skin Biometrics Using Image Processing
Prashant P. Patavardhan & Ashwini C. Kolamkar
Department of Electronics and Communication, Gogte Institute of Technology, Belgaum, Karnataka E-mail : [email protected], [email protected]
Abstract- Human skin exhibits a wide range of colour and texture that differs from one individual to another.
Colour and texture are the attributes used for describing the human skin. These features are inherited by a child, either from its parents or grandparents or both. They also depend on external factors such as climate, region, age, food, etc. The study focuses in determining the colour and texture dominance in a child by using image processing techniques. These features are extracted using RGB colour space and by calculating first order statistical measures such as mean, standard deviation, variance and entropy of the captured images. The Fuzzy Logic Toolbox from MATLAB is used to classify and study the analysis of uncertainty in skin colour and textural features for genetic dominance classification.
Keywords – Colour space, first order statistics, fuzzy, skin colour and texture inheritance.
I. INTRODUCTION
Human skin is the largest organ of the body and performs various functions essential to sustain the body. The appearance of skin is vital in computer graphics and vision [1]. The way the skin appears depends on the optical properties of light that are reflected from the surface of the skin. The appearance of skin can be classified as micro scale, meso scale and macro scale. Micro scale consists of cellular level elements and skin layers. Meso scale consists of skin and skin features. Macro scale consists of body regions and body parts. Meso and macro scales of the skin provide the necessary knowledge for application of skin in computer vision and graphics. Some of the studies conducted on human skin in image processing include detection of skin, photorealistic rendering and diagnosis of skin diseases and disorders [2]-[13].
Section II consists of a brief description of various texture models and colour spaces used for feature extraction. Section III gives a brief introduction on Fuzzy Logic, which is used for classification. Section IV briefly explains human skin components. Section V includes methodology for analysis of skin colour and texture for genetic dominance and Section VI discusses the results.
II. TEXTURE MODELS AND COLOURSPACES
A. Texture Models
A human defines texture depending upon his/her own perception [14]. In order to identify these perceived qualities, mathematical models are needed to be built. These include Statistical methods, Geometrical methods, Model based methods and signal processing techniques.
For the Statistical Methods one of the defining qualities of texture is the spatial distribution of gray values. This can be done using Co-occurrence Matrices and Autocorrelation Features. Spatial gray level co-occurrence estimates image properties related to second-order statistics. Reference [15] suggests the use of Gray Level Co-occurrence Matrices (GLCM).
An important property of many textures is the repetitive nature of the placement of texture elements in the image and this is used to determine the autocorrelation features. The autocorrelation function of an image can be used to assess the amount of regularity as well as the fineness/coarseness of the texture present in the image. First order statistics like mean, median, mode, standard deviation, variance, entropy, etc can also be used for analysis of texture in an image.
Texture analysis based on geometrical methods is characterized by their definition of texture as being composed of texture elements or primitives. Voronoi tessellation and structural methods come under geometrical methods. Voronoi tessellation has desirable properties in defining local spatial neighbourhoods and these local spatial distributions are reflected in the shapes of the Voronoi polygons.
Structural texture analysis consists of extraction of the texture elements and inference of the placement rule.
Model based texture analysis methods are based on the construction of an image model that can be used to describe texture and also to synthesize it. The model parameters capture the vital perceived qualities of texture. The models include Markov random fields (MRFs) and Fractals. Markov random fields (MRFs)
have been popular for modelling images. They are able to capture the local (spatial) contextual information in an image.
These models assume that the intensity at each pixel in the image depends on the intensities of only the neighbouring pixels. Fractals make use of the property that many natural surfaces have a statistical quality of roughness and self-similarity at different scales. Self-similarity across scales in fractal geometry is a crucial concept. The fractal dimension gives a measure of the roughness of a surface. A rough texture results in a large fractal dimension. However the computation of fractal dimension is difficult as most textured surfaces have statistical variation.
The signal processing techniques include Spatial Domain Filters, Fourier domain filtering, Gabor and Wavelet models. Fine textures tend to have a higher density of edges per unit area than coarser textures and this is used in Spatial Domain Filters. The measurement of edginess‟ is usually computed by simple edge masks such as the Robert‟s operator or the Laplacian operator. The Fourier transform is an analysis of the global frequency content in the signal.
For applications that require the analysis to be localized in the spatial domain Gabor and Wavelet models are used.
B. Colour space
Colour is the perceptual result of light having wavelength from 400 to 700 nm that is incident upon the retina [16]. The human retina consists of three types of colour photoreceptor cone cells, that respond to incident radiation with different spectral response curves. To describe a colour, three components are essential and adequate to describe a colour as there are exactly three types of colour photoreceptors. Colour vision is inherently trichromatic.
Colour is extremely subjective and personal [17]. To try to attribute numbers to the brains reaction to visual stimuli is very difficult. The aim of colour spaces is to aid the process of describing colour, either between people or between machines or programs. A colour space is a method by which colour can be specified, created and visualised. A colour gamut is the area enclosed by a colour space in three dimensions.
Choosing a colour space depends on factors such as the intended application, whether it is device dependent or device independent, whether navigating through the colour space and creating desired colours is perceptually linear or intuitive. A device dependent colour space is one where the colour produced depends both the parameters used and on the equipment used for display. A device independent colour space is one where a set of parameters will produce the same colour on whatever equipment they
are used. Table I depicts the various colour spaces used along with its device dependency and visual perception.
TABLE I : COLOUR SPACES Colour Space Device
Dependent
Visual Perception
RGB Yes Non-linear, Semi-
intuitive
CMYK Yes Non-linear,
unintuitive
HSL Yes Non-linear,
extremely intuitive YIQ, YUV,
YCbCr, YCC
Yes Quite unintuitive
CIE No Nearly linear
III. FUZZY LOGIC
A fuzzy set is a set containing elements that have varying degrees of membership in the set [18]. A fuzzy set is a set of ordered pairs which is given by:
A=((x, µA (x)) : x ε X)
Where X is a universal set and µA (x) is the grade of membership of the object x in A (usually 0≤
µA (x)≤1).
A membership function µA (x) is characterized by µA : X→ (0, 1) where X is the universe of discourse, x is a real number describing an object or its attribute and each element of X is mapped to a value between 0 and 1. A membership functions allow us to graphically represent a fuzzy set.
Fig. 1: Triangular Membership function for fuzzy set A
The evaluation of type of membership function performed in [19] shows that triangular membership gave best drive performance for the induction motor drive compared to triangular, two sided gaussian, bell shaped gaussian, polynomial-PI and sigmoidal membership functions. Trapezoidal membership function showed a close response to triangular membership function. The fuzzy set operations include union, intersection and complement. Consider two fuzzy sets A and B on the universe X. For an element x, the following operations are defined as shown in Fig. 2 to Fig. 4:
X µ
1 A
Fig. 2: Union µAᴗB (x)=µA (x) v µB (x)
Fig. 3: Intersection µAᴖB (x)=µA (x) ʌ µB (x)
Fig. 4: Complement µĀ (x)=1 - µA (x)
The fuzzifier transforms the crisp set values to fuzzy sets by applying fuzzification function. The rules and inference engine are the main component of fuzzy logic system which simulates the human reasoning process by making fuzzy inference on the inputs with IF THEN rules. Mamdani style inference is used for fuzzification and for defuzzification centroid method is used. The aggregation method used is max (maximum).
IV. HUMAN SKIN AND ITS COMPONENTS
Hemoglobin, oxyhemoglobin, melanin and carotenoids are the four chromophores responsible for the varying colours in human skin [20]. Absorbtion of specific wavelengths of light and reflection of red light by hemoglobin and oxyhemoglobin gives a pinkish hue to the skin. Melanin gives a range of shades of brown and carotenes are source of yellow- orange pigmentation.
Human skin colour is primarily due to the levels of just one chemically inert and stable visual pigment known as melanin that is responsible for producing all shades of humankind [21]. Melanin is produced by cells called melanocytes in a process
called melanogenesis by the oxidation of the amino acid tyrosine under the influence of enzyme tyrosinase which creates the colour of skin, eyes, and hair shades.
Melanocytes produce two types of melanin: eumelanin and pheomelenin. Eumelanin (brown-black) is found in hair and skin, and the hair colours grey, black, yellow, and brown. Pheomelanin, ( pink to red hue) is found in particularly large quantities in red hair, lips.etc.
Human skin decays soon after death, leaving no trace of its composition or colour. This makes it difficult for the evolutionary biologists to understand the evolution of human skin. Eumelanin is a natural sunscreen and a protective means that has been essential in human evolution [22]. The ultra- violet(UV) rays consist of UVA and UVB rays which are medium wavelength and long wavelength respectively. UVA penetrates through the superficial layers of skin and causes damage to DNA, collagen and elastin molecules responsible for skin‟s strength and elasticity. This damage if cumulative, can lead to skin cancer, skin thickening and wrinkles. UVB cannot penetrate the skin as deeply as UVA, but can also cause severe damage to the DNA in the skin that can lead to skin cancer. UVR is responsible for the process of making vitamin D in the skin, which is one of its positive effects. The author concluded that skin pigmentation is a product of evolution by natural selection and an ideal tool for teaching evolution.
Also, skin colour does not define human race, and skin tones have evolved multiple times independently in human history.
Genetics of skin colour [23] suggests that a multi-factorial pattern of inheritance is under the control of an interacting set of genes and environmental influences, eg. skin darkening (tanning) induced by sunlight. Children may have a lighter or darker skin shade than either of their parents. It is thought that more than 20 genes affect skin colour in humans. The inheritance of genetic variation is easier to follow when only one pair of genes affects a characteristic, for example in albinism (monogenic inheritance), and one can predict the likely outcome.
But when many genes are involved, it becomes difficult to predict the resultant pigmentation. A gamut of skin colours may be seen between parents and their individual children, depending on the underlying genetic variation in the melanin-associated gene pairs.
Human skin colour is controlled by the complex interplay of many different genetic variants, most of which control production and cellular arrangement of melanin and has also been moulded by the environment in different regions of the world over thousands of years [24]. Hence it indicates that skin colour certainly has a biologic or genetic basis. The author suggests that inorder to identify and confirm
the role of pigmentation genes, genetic studies of populations and families is needed.
V. METHODOLOGY
Human skin colour and texture is a polygenic inheritance which consists of involvement of more than one gene in determing the phenotype. Other factors include hormones, physical and mechanical irritations, substance irritants, allergic reactions, climate and air pollution, lifestyle and mental and emotional balance. For the study, the factor of inheritance is considered and is conducted in vivo by capturing images. It attempts to find, whether the skin colour and texture inherited by a child is either from the parents or grandparents or both. The study does not quantify the percentage of inheritance but attempts to find whether such a dominant inclination of colour and texture inheritance can be found, using only image processing techniques. Firstly, to begin with the study, families having three generations are identified. The family members constitute the paternal grandparents, parents and children. Next, the anatomical body site chosen is the upper inner forearm, as it is protected by clothing against the effects of sun tanning. The source of illumination is the natural light and the images are captured using Nikon Coolpix 8 Megapixel digital camera from a distance of 10cm. The background colour chosen is black in the captured images.
The images captured are in JPEG format. The images are named as PGF, PGM, F, M, GC1 and GC2.
Preprocessing of the images include cropping the image to size of 128X128 pixel size and applying Contrast-limited adaptive histogram equalization (CLAHE) . Contrast adjustment is used for preprocessing as the human eye is sensitive to contrast rather than absolute pixel intensities [25]. The colour space considered is RGB as it is easy to implement.
For the description of texture, first order statistical measures namely mean, standard deviation, variance and entropy are calculated.
Fig. 5: 128x128 dimension images of the upper ineer forearm of five families (F1, F2, F3, F4 and F5). From
left to right: Paternal Grandfather (PGF), Paternal Grandmother (PGM), Father (F), Mother (M) and
Grandchildren (GC1) (GC2).
The code for feature extraction is in MATLAB. Both colour and texture feature extraction are incorporated together in one code for each of the planes in RGB colorspace individually, to find the first order statistics mentioned above. The values so obtained are normalised so that they lie in the range between 0 and 1. Using Microsoft Excel, all the data is carefully sorted. For classification Fuzzy Logic Toolbox from MATLAB is used. All the four parameters calculated in each plane of RGB colourspace are fed to the Fuzzy Inference System (FIS) and triangular membership function is used. Four membership functions are defined using the minimum and maximum values of all the five families combined for each of the four parameters. The membership functions consist of the values obtained from PGF, PGM, F and M. By providing the input of GC1 and GC2, the output shows the preference of GC1 and GC2 to the input values of PGF, PGM, M and F. Based on the output preference of the four parameters, a majority of the same is tabulated and indicated as a dominance in skin colour and texture of the grandchild with respect to its parents or grandparents or both.
VI. RESULTS AND DISCUSSIONS
Table II shows the tabulated results of all four parameters namely mean, standard deviation, variance and entropy for each of the planes of RGB colourspace calculated for all five families. For GC1, in all families except F2, the table indicates a clear majority of certain members of the family over the others. GC2 in family F5 shows a majority towards M whereas in F3 it is not clear. GC1 of F1, F3 and F4 show a parental inheritance whereas that of F2 and F5 shows grandparental inheritance. The outcome is obtained only by a majority. It does not quantify the skin of the grandchild to be exactly similar to the family members indicated in the results. It only shows more inclination to those family members.
The observations made are with respect to only one colourspace. The study can be further conducted for other colour spaces. For texture extraction, other methods can also be implemented. The application areas of this research could include the study of evolution of human skin, realistic modelling of skin texture in computer graphics and animation. The factors such as lifestyle, food intake, climate, region and age can be included for more accurate results. The applications may include a study in the evolution of human skin, using skin as a biometric, studying skin ageing, skin image retrieval from a database of
images. Future studies may include incorporating one or more of these factors with the results already obtained.
GC1
FAMILY PLANE MEAN STDEV VAR ENT M
A J
F1
R N M M M
G PGM M N M M
B PGF M M M
F2
R N F F F
P G M
G PGM/
M
PGM F PGF/PGM
B PGM/
M PGM PGF PGF
F3 R M F M F
G PGM/ F
M M F PGF/
PGM/F
B PGM/
M F N PGF/
F
F4 R F F F F
G PGM F F F F
B PGM
/M F F F
F5 R PGF PGF/
PGM
PGF /PGM/F
PGF/
PGM/F P G F
G PGF/
F
PGF/
PGM
PGF/
PGM
PGF/
PGM
B PGF PGF/
PGM
PGF/
PGM PGF
C2
FAMILY PLANE MEAN STDEV VAR ENT MAJ
F3
R M F F F
G PGM PGM PGF/ NC
PGM PGF
B PGF PGM M PGF
F5
R N F M F
G PGM/ M
M M M F
B PGM M M M
Table II: Observed results of GC1 and GC2 in all the five families for RGB colour space and first order statistics viz. mean, standard deviation, variance and entropy. N indicates none, NC indicates Not Clear Dominance and MAJ indicates MAJORITY.
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