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# Mathematical Model of Skin Color for Face Detection

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### Mathematical Model of Skin Color for Face Detection

1. 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. 2. Introduction • Face detection is a preprocessing step of facial recognition system (Essential)
3. 3. Introduction • Goal: localize face(s) in digital image and/or in real time video
4. 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. 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. 6. Face in Colour Spaces RGB space HSB space YCbCr space
7. 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. 8. Sample of generated skin colour model
9. 9. Skin distribution in Colour Spaces
10. 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. 11. Visual Result
12. 12. Experiments
13. 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. 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. 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