1. Introduction to Skin and Face Detection
Aleksey Deykin

2. Introduction What it is Applications Requirements
Automatic computer recognition of faces and skin tone
Applications
Anything from security and law enforcement to assisting the elderly and visually impaired
Requirements
Fast, accurate, and lighting and ethnicity invariant

3. Skin Color Detection Provide a sample of skin tone
Calculate average color (RGB)
Scan images pixel by pixel
If color matches, color pixel red

4. RGB
The most commonly used color space in digital images. It encodes colors as an additive combination of 3 primary colors: red (R), green (G) and blue (B)
Red: rgb(255,0,0)
Blue: rgb(0,255,0)
Green: rgb(0,0,255)
Black: rgb(0,0,0)
White: rgb(255,255,255)

5. Simple Skin Detection

6. Improved Skin Detection

7. Improved Skin Detection

8. Improved Skin Detection

9. Algorithm Loop through every pixel of the sample rectangle
Add pixel’s RGB channels to a vector
Calculate average RGB value (skin tone)
Loop through every pixel of the image
If R±40 and G±40 and B±40 for rectangle 1, or
If R±40 and G±40 and B±40 for rectangle 2
Color the pixel red (skin detected)

10. Challenges & Limitations
Slow
O(xy)
80 seconds per 100 skin detections, or 0.8 seconds per image (400x608)
As resolution doubles, computing time quadruples
Color-dependent
Black & white pictures problematic
Ethnicity dependent
Needs contrasting background

11. Challenges & Limitations

12. Further Research Different color space? YCbCr
Used in video and digital photography systems due to its ability to encode and compress RGB information. Stores luminance separately.

13. Face Detection Viola-Jones algorithm Feature-based vs pixel-based
Detector scans input at multiple scales, starting with a base of 24x24 pixels, such that a 384 by 288 pixel image is scanned at 12 scales with a 1.25x step
AdaBoost learning algorithm (thousands of faces to train)
First selected feature is usually around the eyes (usually darker area) - if eyes are not visible, algorithm usually fails
95% detection (1 in falsepositive) – 15 fps

14. Face Detection Results

15. Challenges & Limitations
Trained on front-facing upright faces and is only reliable for faces rotated around ±15 degrees in plane and ±45 degrees out of place (toward a profile view)
Fails for overexposed (bright) backgrounds
Heavily occluded faces not detected

16. Further Research Combine skin and face detection?
Pre-screen images for skin, then run face detection over skin regions
Run both algorithms, one is bound to find a face
Extend skin detection?

17. Detect skin… And faces

18. Conclusion Simple algorithm to detect skin
Slow and highly dependent on lighting
Possible to improve results with different color space
Faces naturally form detectable ovals
Wear shades to protect privacy

19. References
Elgammal, A., Muang, C., and Hu, D Skin Detection - a Short Tutorial. Rutgers University, Piscataway, NJ. May 17, 2012.
Shah, M. A. An Introduction to Wavelets and the Haar Transform. May 17, 2012.
Soetedjo, A., Yamada, K Skin Color Segmentation Using Coarse-to- Fine Region on Normalized RGB Chromaticity Diagram for Face Detection. IEICE Trans. Inf. & Syst., Vol.E91-D, No.10 October 2008.
Szeliski, R Computer Vision: Algorithms and Applications. May 17, pp
Viola, P., Jones, M. J Robust Real-Time Face Detection. International Journal of Computer Vision 57(2), pp , 2004.