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Mathematical Model of Skin
Color for Face Detection
Setiawan Hadi, Adang Suwandi A,
Iping Supriana S, Farid Wazdi
Universitas Padjadjaran, Bandung, Indonesia
Institut Teknologi Bandung, Indonesia
Introduction
• Face detection is a preprocessing step of
facial recognition system (Essential)
Introduction
• Goal: localize face(s) in digital image and/or in
real time video
Skin-based Face Detection
• Skin important element in detecting image
that contain skin or skin-like region
• Skin is special
– covers most of the face image area
– skin of different people appears to vary over a wide
range, however the differ is much less in colour
(chromaticity) than brightness
– detection of skin area in digital image are more
practical and easy to implement.
Our Research Approach
• Skin colour is represented in 3 colour
space (rg, HSB and YCbCr)
• Using mathematical model that is
generated from face images
• Implement morphological filters for
enhancing face image
• Apply 4-neigbourhood ellipse
representation for localizing face
• Using local face databases for experiment
Face in Colour Spaces
RGB space
HSB space
YCbCr space
Generating Face Skin Model
• Calculate mean and covariance
chromaticity of training images for each
colour space
• Training images are prepared semi-
manually
Mk =
nX
i = 1
1
±i
Ti
Mk =
1
±1
T1 +
1
±2
T2 + ¢¢¢+
1
±n ¡ 1
Tn¡ 1 +
1
±n
Tn
Sample of generated skin colour model
Skin distribution in Colour Spaces
Face Detection
Algorithm
where Pski n (i; j ) is probability of
pixel P as skin pixel if included
in distribution skin model DM k
for every colour spaces Rn .
Pski n (i; j ) = Pski n (i; j ) 2 DM k
8 P(i; j ) ^ 8 Rn
Visual Result
Experiments
Concluding remarks
• Skin colour is modelled using using mean-
covariance characteristics
• Skin colour is represented in 3 colour space
• Skin model is used for face detection, with
support morphological filter dan 4-neigborhood
ellipse generation
• Experiment has been performed using 7 sets of
face database, >>3000 face images
• Accuracy needs to be improved
Next Work
• Multiple image detection
• Symmetry and features detection
• Adding geometric-based detection to
increase detection accuracy
• Algorithm improvement for efficient yet
faster detection
• Realtime face detection
• Face recognition module
Mathematical Model of Skin
Color for Face Detection
Setiawan Hadi, Adang Suwandi A,
Iping Supriana S, Farid Wazdi
Universitas Padjadjaran, Bandung, Indonesia
Institut Teknologi Bandung, Indonesia

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Mathematical Model of Skin Color for Face Detection

  • 1. Mathematical Model of Skin Color for Face Detection Setiawan Hadi, Adang Suwandi A, Iping Supriana S, Farid Wazdi Universitas Padjadjaran, Bandung, Indonesia Institut Teknologi Bandung, Indonesia
  • 2. Introduction • Face detection is a preprocessing step of facial recognition system (Essential)
  • 3. Introduction • Goal: localize face(s) in digital image and/or in real time video
  • 4. Skin-based Face Detection • Skin important element in detecting image that contain skin or skin-like region • Skin is special – covers most of the face image area – skin of different people appears to vary over a wide range, however the differ is much less in colour (chromaticity) than brightness – detection of skin area in digital image are more practical and easy to implement.
  • 5. Our Research Approach • Skin colour is represented in 3 colour space (rg, HSB and YCbCr) • Using mathematical model that is generated from face images • Implement morphological filters for enhancing face image • Apply 4-neigbourhood ellipse representation for localizing face • Using local face databases for experiment
  • 6. Face in Colour Spaces RGB space HSB space YCbCr space
  • 7. Generating Face Skin Model • Calculate mean and covariance chromaticity of training images for each colour space • Training images are prepared semi- manually Mk = nX i = 1 1 ±i Ti Mk = 1 ±1 T1 + 1 ±2 T2 + ¢¢¢+ 1 ±n ¡ 1 Tn¡ 1 + 1 ±n Tn
  • 8. Sample of generated skin colour model
  • 9. Skin distribution in Colour Spaces
  • 10. Face Detection Algorithm where Pski n (i; j ) is probability of pixel P as skin pixel if included in distribution skin model DM k for every colour spaces Rn . Pski n (i; j ) = Pski n (i; j ) 2 DM k 8 P(i; j ) ^ 8 Rn
  • 13. Concluding remarks • Skin colour is modelled using using mean- covariance characteristics • Skin colour is represented in 3 colour space • Skin model is used for face detection, with support morphological filter dan 4-neigborhood ellipse generation • Experiment has been performed using 7 sets of face database, >>3000 face images • Accuracy needs to be improved
  • 14. Next Work • Multiple image detection • Symmetry and features detection • Adding geometric-based detection to increase detection accuracy • Algorithm improvement for efficient yet faster detection • Realtime face detection • Face recognition module
  • 15. Mathematical Model of Skin Color for Face Detection Setiawan Hadi, Adang Suwandi A, Iping Supriana S, Farid Wazdi Universitas Padjadjaran, Bandung, Indonesia Institut Teknologi Bandung, Indonesia