Probabilistic Formulation for Skin Detection

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Probabilistic Formulation for Skin Detection

• Sanun Srisuk
• 42973003

Seminar I
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Outline

• 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

• Skin Detection using Fuzzy Theory
• Skin Detection using Color Statistics

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Skin Detection using Fuzzy Theory

• 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
• Manually select skin regions in each image.
• Prepare a table of 92x140 entries to record the
  two dimensional chromatic histogram of skin
  regions, and initialize all the entires with
  zero.
• 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.
• 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
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Proposed Skin Detection
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Proposed Skin Detection (cont.)
Maximum a posteriori (MAP)
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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 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

• 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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The End.