![](bg2.png)
2 COLOR
Each image is represented using three primaries of the
color space chosen. Most digital images are stored in
RGB color space. RGB color space is represented
with red (R), green (G), and blue (B) primaries and
is an additive system. RGB color space is not per-
ceptually uniform, which implies that two colors with
larger distance can be perceptually more similar than
another two colors with smaller distance, or simply
put, the color distance in RGB space does not repre-
sent perceptual color distance.
RGB is one of the most widely used colorspaces,
however, high correlation between channels, signif-
icant perceptual non-uniformity, mixing of chromi-
nance and luminance data make RGB not a very
favorable choice for color analysis and colorbased
recognition algorithms.
Normalized RGB is a representation, that is eas-
ily obtained from the RGB values by a simple
normalization procedure: r =
R
R+G+B
; g =
G
R+G+B
; b =
B
R+G+B
. The three normalized com-
ponents r, g and b are called pure colors; they con-
tain no information about the luminance. Because
(r+g+b = 1), the third componentdoes not hold any
significant information and can be omitted, reducing
the space dimensionality. It is enough to use only two
components r and g to completely describe the skin
color space.
Y C
r
C
b
is an encoded nonlinear RGB signal for
image compression work. Color is represented by lu-
minance, computed from nonlinear RGB (Poynton,
1995), constructed as a weighted sum of the RGB
values, and two color difference values C
r
and C
b
that
are formed by subtracting luminance from RGB red
and blue components.
Y C
b
C
r
color space has been defined in response
to increasing demands for digital algorithms in han-
dling video information, and has since become a
widely used model in a digital video. It belongs
to the family of television transmission color spaces.
These color spaces separate RGB into luminance and
chrominance information.
Y = 0, 299R + 0, 587G + 0, 114B
C
r
= 0, 713(R − Y ) (1)
C
b
= 0, 564(B − Y )
HSI, HSV, HSL - Hue, Saturation, Intensity
(Value, Lightness), color spaces describe color with
intuitive values. Hue defines the dominant color (such
as red, green, purple and yellow) of an area, satura-
tion measures the colorfulness of an area in propor-
tion to its brightness (Poynton, 1995). The ”inten-
sity”, ”lightness” or ”value” is related to the color lu-
minance. The intuitiveness of the color space com-
ponents and explicit discrimination between lumi-
nance and chrominance properties made these color
spaces popular in the works on skin color segmenta-
tion (Zarit, B.D., Super, B.J., Quek, F.K.H., 2002),
(Sigal, L., Sclaroff, S., Athitsos, V., 2000).
H = arcsin(
(C
r
− 128)
128 · S
); V =
Y
256
S =
p
(C
r
− 128)
2
+ (C
b
− 128)
2
128
(2)
3 SKIN-COLOR MODEL
3.1 Explicitly Defined Skin Region
The statistical skin-color model is generated by means
of a supervised training, using a set of skin-color re-
gions, obtained from a color human body database.
Such images were obtained from people of different
races, ages and gender,with varying illumination con-
ditions.
One method to build a skin classifier is to define
explicitly (through a number of rules) the boundaries
skin cluster in some color space. For example (Peer,
P., Kovac, J., Solina, F., 2003) (R, G, B) is classified
as skin if:
R > 95 and G > 40 and B > 20 and
max{R, G, B} − min{R, G, B} > 15 and
|R − G| > 15 and R > G and R > B.
We have found that skin-color region can be iden-
tified by the presence of a certain set of chrominance
(ie C
r
and C
b
) values that is narrowly and consis-
tently distributed in the Y C
r
C
b
color space. We de-
note RC
r
and RC
b
as the respective ranges of C
r
and
C
b
values that correspond to skin color, which sub-
sequently define our skin-color reference map. The
ranges that we found to be the most suitable for all
the input images that we have tested are RC
r
=
[133, 173] and RC
b
= [77, 127]. This map has been
proven, in our experiments, to be very robust against
different types of skin color (Figure 1).
With this skin-color reference map, the color seg-
mentation can now begin. Since we are utilizing only
the color information, the segmentation requires only
the chrominance component of the input image. The
output of the color segmentation, is a skin bitmap SM
described as
SM (x, y) =
1 if C
r
(x, y) ∈ RC
r
∩ C
b
(x, y) ∈ RC
b
0 otherwise
(3)
HUMAN SKIN COLOR DETECTION AND APPLICATION TO ADULT IMAGE DETECTION
389