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Introduction to Skin and Face Detection

Introduction to Skin and Face Detection. Aleksey Deykin. Introduction. What it is Automatic computer recognition of faces and skin tone Applications Anything from security and law enforcement to assisting the elderly and visually impaired Requirements

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Introduction to Skin and Face Detection

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  1. Introduction to Skin and Face Detection Aleksey Deykin

  2. Introduction • What it is • 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±40and 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 14084 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. 2009. Skin Detection - a Short Tutorial. Rutgers University, Piscataway, NJ. http://www.cs.rutgers.edu/~elgammal/pub/skin.pdf. May 17, 2012. • Shah, M. A. An Introduction to Wavelets and the Haar Transform. http://www.cs.ucf.edu/~mali/haar/. May 17, 2012. • Soetedjo, A., Yamada, K. 2008. 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. 2010. Computer Vision: Algorithms and Applications. http://szeliski.org/Book/. May 17, 2012. pp. 664-665. • Viola, P., Jones, M. J. 2003. Robust Real-Time Face Detection. International Journal of Computer Vision 57(2), pp. 137-154, 2004.

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