![](bg7.png)
Table2:
The performance of our skin detection algorithm
in comparison with four state-of-the-art methods at a false
positive rate of ~0.17. Our results are the average of three
performances in different sets of randomly selected
images.
Methods Detection rate False positive
HSU 73.0% 17%
Jones1 85.0% 17%
Jones2 86.5% 17%
SkinDiff 88.1% 17%
Our method 92.1% 17.2%
Even though a considerable amount of
processing is employed for implementing our
method, a reasonable high processing speed is
achieved. It is worth to mention that the cost of
processing different brightness levels implemented
by LUT (look-up table), or incorporating
neighborhood information implemented using
integral image representation, was even less than the
transformation of the RGB color space to some other
color spaces like CIELAB or HIS, which have been
used by various researchers. However, the cost of
employing an adaptive thresholding strategy,
especially the entropic ones, is remarkable. The
computational time of our method for processing a
320x240 image is approximately 1.1s on a 3.06 GHz
CPU. However, this time is decreased to 0.28s by
employing a fixed value threshold.
7 CONCLUSION AND FUTURE
WORK
In this paper a novel skin detection algorithm was
proposed for handling the real-world situations, such
as bad lighting conditions or skin-like background
colors. Three contributions of our work are: (i)
processing each pixel in different brightness levels
for handling the problem of illumination variation;
(ii) presenting a fast and simple method for
incorporating the neighborhood information in
processing each pixel; and (iii) presenting a
comparative study on thresholding the skin
likelihood map, and employing local entropy
technique for binarizing our skin likelihood map.
The details of our method are described and the
detection performance is compared with some state-
of-the-art methods using a set of real-world images,
obtaining better results.
One of the directions that we are considering for
future work is to incorporate texture and shape into
our skin detection method. Furthermore, we intend
to apply our skin detection strategy to additional
applications such as nudity detection and adult
image filtering.
ACKNOWLEDGMENTS
The authors would like to acknowledge the
Communication and Information Technology
Ontario (CITO) for partially supporting this work.
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