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Face Detection on Similar Color Images

Face Detection on Similar Color Images. Scott Leahy EE368, Stanford University May 30, 2003. Problem Statement. Goal: Find faces in an image All images are in color Images all contain a similar background Images have a similar number of faces

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Face Detection on Similar Color Images

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  1. Face Detection on Similar Color Images Scott Leahy EE368, Stanford University May 30, 2003 EE368: Digital Image Processing Bernd Girod

  2. Problem Statement • Goal: Find faces in an image • All images are in color • Images all contain a similar background • Images have a similar number of faces • Faces are all on approximately the same scale • Design an algorithm which takes advantage of these facts EE368: Digital Image Processing Bernd Girod

  3. Sample Image EE368: Digital Image Processing Bernd Girod

  4. Detection Procedure • Steps Involved: • Skin Detection • Morphological Processing • Template Matching • Face Coordinate Selection EE368: Digital Image Processing Bernd Girod

  5. Skin Detection • Pixel by pixel, make a decision on the input based on the output • i = {skin, non-skin} • v = vector in color space (HSV, RGB, …) • Treat the problem like a digital communications problem • Create a MAP Detector ? i v EE368: Digital Image Processing Bernd Girod

  6. Skin Detection (cont’d) • MAP Detection • Minimize probability of error: • Maximize p(i|v) over all inputs i • Often p(i|v) is not known, but: • p(i|v) = p(v|i) * p(i) / p(v) (Bayes’ Rule) • p(v|i) and p(i) are more often known in a system EE368: Digital Image Processing Bernd Girod

  7. Histograms EE368: Digital Image Processing Bernd Girod

  8. 2 Dimensional PDF • Used only Hue and Saturation for MAP detector EE368: Digital Image Processing Bernd Girod

  9. 3 Dimensional PDF • Used all 3 coordinates for MAP detector EE368: Digital Image Processing Bernd Girod

  10. “Closing” Step • Pseudo-Closing Step: • Dilation • Filling • Erosion EE368: Digital Image Processing Bernd Girod

  11. Template Matching • Template matching involves convolving the image with some template • The average of the image being tested must be subtracted to eliminate biasing toward brighter areas • Only one template used due to similar size and shape of faces in all images EE368: Digital Image Processing Bernd Girod

  12. Trial Templates • Tried 4 templates, tweaking threshold until the best results were obtained EE368: Digital Image Processing Bernd Girod

  13. Face Selection • Labeled all regions • Selected only regions with areas bigger than some threshold • Found the centers of the remaining regions and returned those as the results of the algorithm EE368: Digital Image Processing Bernd Girod

  14. Results EE368: Digital Image Processing Bernd Girod

  15. Conclusions • Skin Detection and Closing • Takes advantage of images being in color • Takes advantage of similar statistics in the images • Template Matching and Face Selection • Takes advantage of similar size and shape to faces • Result: ~85% success rate EE368: Digital Image Processing Bernd Girod

  16. EE368: Digital Image Processing Bernd Girod

  17. EE368: Digital Image Processing Bernd Girod

  18. EE368: Digital Image Processing Bernd Girod

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