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Mathematical Model of Skin Color for Face Detection
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Mathematical Model of Skin Color for Face Detection

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  • 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
  • 11. Visual Result
  • 12. Experiments
  • 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

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