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Probabilistic Formulation
for Skin Detection
Sanun Srisuk
42973003
Seminar I
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Outline
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Problem Statement
Literature Review
Proposed Skin Detection
Experimental Results
Conclusions
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Problem Statement
Face Detection Methods
•Shape Analysis
•Fuzzy Pettern Matching
•Neural Networks
•SVM
•Hausdorff Distance
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Literature Review
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Skin Detection using Fuzzy Theory
Skin Detection using Color Statistics
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Skin Detection using Fuzzy Theory
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Wu et al. [12] propose a method for skin
detection using fuzzy theory.
SCDM and HCDM are skin and hair color
models.
The perceptually uniform color system (UCS) is
used for color representation.
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Skin Detection using Fuzzy Theory (cont.)
SCDM
1. Manually select skin regions in each image.
2. Prepare a table of 92x140 entries to record the
two dimensional chromatic histogram of skin
regions, and initialize all the entires with zero.
3. Convert the chromaticity value of each pixel in
the skin regions to UCS, and then increase the
entry of the chromatic histogram corresponding
to it by one.
4. Normalize the table by dividing all entries with
the greatest entry in the table.
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Skin Detection using Fuzzy Theory (cont.)
HCDM
Skin and Hair Color Detectors
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Skin Detection using Fuzzy Theory (cont.)
The method proposed in the Wu et al. scheme
sometimes fails to detect the real face. Reasons
under concern include the followings.
•Illumination: This is because, the luminance
information is used to detect the hair part of faces, the
variance of the illumination color will affect the
detection result.
•Hairstyle: Faces with special hairstyles, such as
skinhead, or wearing a hat, may fail to be detected.
This is because the shape of the skin-hair pattern of
such a face in the image may become quite different
from the head-shape model.
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Skin Detection using Color Statistics
Wang et al. [11] present a fast algorithm that
automatically detects face regions in MPEG
compressed video.
•Bayesian minimum rule is used to classify skin or
nonskin class.
•Classification is performed in YCbCr color model.
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Skin Detection using Color Statistics (cont.)
Bayesian decision rule
Minimum cost decision rule
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Skin Detection using Color Statistics (cont.)
where
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Skin Detection using Color Statistics (cont.)
This algorithm can detects 84 of 91 faces (92%),
including faces of different sizes, frontal and side-view
faces. Detected face regions are marked by white
rectangular frames overlaid on the original video
frames. There are eight false alarms in this
experiment. The algorithm is restricted in several
aspects.
•It can only be applied to color images and videos,
because of the use of chrominance information.
•The smallest faces that are detectable by this
algorithm are about 48x48 pixels (3x3 macroblocks)
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Proposed Skin Detection
In this paper, we
•propose a method for color model selection
using bayesian estimation.
•present an algorithm for color model
combination using fuzzy concept.
•create 1-D and 2-D histograms for skin
pixel classification.
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Proposed Skin Detection
probability of skin or nonskin given C1 and C2
where
are chrominance components.
denotes the skin and nonskin classes.
be the 2-D histogram of skin and nonskin areas.
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Proposed Skin Detection
represents the probability of
given class
is the a priori probability of class
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Proposed Skin Detection (cont.)
Maximum a posteriori (MAP)
A decision function for selecting the chrominance
be the selected chrominance components.
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Proposed Skin Detection (cont.)
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Proposed Skin Detection (cont.)
membership function
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Proposed Skin Detection (cont.)
Skin detection function
where
is the skin color likeness function.
are the minimum and maximum values of the range
of human skin in selected chrominance components.
is the range from a to b and from c to d.
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Proposed Skin Detection (cont.)
is the weighting coefficient associated with
chrominance component
is the probability generated by 1-D histogram.
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Results
Skin detection under
varying illuminations
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Results
Skin detection under
different races
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Results
Original Image
Our proposed method
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Results
HSV [6]
YCbCr [3]
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Results
Original Image
Our proposed method
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Results
HSV [6]
YCbCr [3]
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Results
Original Image
Our proposed method
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Results
HSV [6]
YCbCr [3]
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Results
Original Image
Our proposed method
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Results
HSV [6]
YCbCr [3]
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Conclusions
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The skin and nonskin probabilities are created
from 1-D and 2-D histograms.
Bayesian estimation is used to select
appropriate well-known color models.
Skin detection is performed by fuzzy
membership function and normalized by 1-D
histogram.
The method is proposed for robust skin
detection under varying illuminations and
different races.
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