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RGB Level Face Detection

RGB Level Face Detection. Jian Zhang Miao He Jing Chen May.27th,2002. How to find faces?. Mission Analysis. lots of faces in the images, time-consuming to search the face candidates, some faces got overlapped

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RGB Level Face Detection

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  1. RGB Level Face Detection Jian Zhang Miao He Jing Chen May.27th,2002

  2. How to find faces?

  3. Mission Analysis • lots of faces in the images, time-consuming to search the face candidates, some faces got overlapped • sidelight instead of diffused light. Influence from shade, complexion, rotation of face, and the inhomogeneity of background • 1280*960 high resolution image, slows down the detection speed and more noise

  4. 2-step Algorithm • 1. Skin Toner Masks • 2. Support Vector Machine (SVM)

  5. Skin Toner Masks (1) • Mask based on hue in HSV space

  6. Skin Toner Masks (2) • Mask based on RGB statistics

  7. Skin Toner Masks (3) • Mask for red color

  8. Skin Toner Masks (4) • Mask to remove the ground

  9. Skin Toner Masks (5) • Remove small regions

  10. Skin Toner Masks (6)—Final Mask

  11. SVM Theory(1) • Given the training sample {(xi,di)}, i=1…L, find the Lagrange multipliers {αi}, i=1…L, that maximize the objective function • Subject to the constraints 1) 2) for i=1,2…L Where C is a user-specified positive parameter.

  12. SVM Theory(2) • The discriminate is

  13. Pre-processing • In the tradeoff between speed and accuracy, we choose 12-by-12 and 4 gray scale samples. • Eliminating the effect of side light. • Histogram equalization

  14. Face Samples in RGB,4,3,2 Gray Scale

  15. Training a SVM • a large date set, about 1000 face (different scales and positions) and more than 7000 non-face samples. • takes more than 30 hours on the ISL lab computer to get one set of training result. • From now on,SVM shows its advantage

  16. Teaching and Learning Process(1)

  17. Teaching and Learning Process(2)

  18. Our improvement • Add rotated face into face data set. Thus we can detect these special faces • faces concentrate against non-faces B is an observation constant. Thusenhance the detection accuracy.

  19. Testing Results (1) • Now we need merge the detection points to give the final decision. Model the three-scale detection as a diversity situation like in wireless communication channels.

  20. Testing Results (2) • maximum-ratio diversity • perform dilation to binary image

  21. Final Detection

  22. Further Implementation

  23. Acknowledgement • The authors want to express out thankfulness to Prof. Girod’s excellent instruction. We all learn a lot from this interesting course. • We would also like to thank our teaching assistant, Chuo-ling Chang, for his patience and help.

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