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mentation which, different of color, has been rarely
used for skin classification. Texture-based segmenta-
tion takes into account the positional relations among
pixels in a region of the image. It considers not only
one pixel but its behaviour in a region that is named
as texel. Since the color skin segmentation can be
perturbed by the mentioned conditions, we have con-
sidered in this work comparing it with texture as a
complementary feature.
This paper aims to systematically analyze dif-
ferent color and texture-based skin-detection meth-
ods. The comparison is performed both visually and
quantitatively (using False Positive and False Nega-
tive classification error rates) for the same set of im-
ages. For the color-based skin detection approach, we
use three common spaces (one for each of the men-
tioned types of color representations): RGB, HSV
and YCbCr, respectively. We have used a simple
pixel rule-based for the RGB space, and have pro-
posed a new region-growing algorithm for skin de-
tection which can be similarly employed in the HSV
and YCbCr. For the texture-based skin detection
approach, the Spectral Variation Coefficient (SVC)
(Nunes and Conci, 2007) is applied to estimate skin
region features. Both approaches (color and texture-
based skin-detection) are computationally efficient
and suited for real time applications.
The rest of the paper is organized as follows. Sec-
tion 2 describes the elements involved in the color-
based skin detection methods, as well as a global
pseudo code of the approach. Section 3 describes a
similar presentation for the texture-based approach.
Experimental results for color and texture-based skin
detection methods are presented and compared in
Section 4. Main conclusions and future work are out-
lined in Section 5.
2 COLOR-BASED SKIN
SEGMENTATION
As the human skin seems to have a characteristic
range of color, many skin detection approaches are
based on classifying pixels using their color (Forsyth
and Ponce, 2003). A wide set of color spaces
have been considered to model the skin chrominance
(Vezhnevets et al., 2003)(Kakumanu et al., 2007).
However, according to (Albiol et al., 2001): “for all
color spaces their correspondingoptimum skin detec-
tors have the same performance since the separability
in skin or not skin classes is independent of the color
space chosen”. In other words, the quality of a skin
detection method is more dependent on the proposed
detection algorithm and less on the used color space.
In this section, in order to investigate this state-
ment, three color spaces are used to represent the hu-
man skin: RGB, HSV and YCbCr. For the case of
basic RGB space, we applied the following simple
explicit skin detection algorithm that can be found in
(Kovac et al., 2003) and works on all the image pixels
for uniform daylight illumination:
(R > 95) ∧ (G > 40) ∧ (B > 20)∧
(max(R, G, B) − min(R, G, B) > 15)∧
(|R− G| > 15) ∧ (R > G) ∧ (R > B)
(1)
where R, G and B represent the value of pixel in the
respective RGB color channel with values ranging
from 0 to 255. For HSV and YCbCr spaces, a sim-
ilar approach is followed. We retain luminance infor-
mation by converting the image to gray levels. Next,
we produce an initial oversegmentedimage of regions
by applying a morphological watershed method. For
each of the produced watershed regions their corre-
sponding color histograms for each of the three chan-
nels (in HSV or YCbCr spaces) are computed and
compared using the Battachariyya distance (Kailath,
1967) to those corresponding to previously trained
skin histograms. If these histograms are similar, the
region is considered as “skin region” and used as seed
for a region growing algorithm applied on neighbour
non-skin regions. Finally, the explicit skin detection
algorithm in Fig. 1 is applied to each pixel of de-
tected skin regions to discard False Positive (FP) skin
regions when the percentage of skin pixels in a region
is above an experimental threshold. The global pro-
posed color based skin detection method is outlined
by the pseudo code shown in Fig. 1. Some important
remarks on this algorithm are the following ones.
• To recognize the skin pixels in images, we per-
formed a training stage on the system. For this
task, we obtained different skins histograms using
a set of images where 2,314 skin fragments were
manually extracted (i.e. only skin pixels were
considered). A histogram was created for each
of the three channels in each considered color
space (RGB, HSV and YCbCr, respectively) and
its number of histogram bins was set to 10.
• The Battacharyya distance (Kailath, 1967) was
used to compare the three channel histograms
in a given color space of a test region with the
corresponding ones of trained skin regions his-
tograms to decide if a region can be considered
or not as skin region. This distance measures
the similarity of two discrete probability distribu-
tions. Given the probability distributions obtained
from the corresponding histogram vectors, p and
q respectively, over the same domain X, the Bat-
tachariyya coefficient BC is defined as:
COMPARING COLOR AND TEXTURE-BASED ALGORITHMS FOR HUMAN SKIN DETECTION
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