1. A fast algorithm for tracking human faces based on chromatic histograms Pattern Recognition Letters, 1999 Speak: M. Q. Jing 4/23/2001 國立交通大學 自動化資訊處理 lab

2. Outline Introduction Chromatic histogram operations Creation of a chromatic histogram Backprojection of a chromatic histogram Face tracking algorithm Localization of a face region Tracking the face in the sequences Experimental results

3. Introduction The solutions on motion Motion Estimate (Haralick, Horn, Desilva) Precise approximation of the motion [DrawBack] heavy computation Color histogram approach Insensitive to rotation,scaling,deformation Immune to the noises and cammera ’ s small changing.

4. Chromatic histogram operations Color Model: HSI Color Model

5. Why do we use HSI The chromaticity and intensity is separated Reduce the effect of illumination Reduce the dimension of histogram from 3 to 2 Speedup the process

6. Creation of a chromatic histogram Each of the H and S axes are quantized into 32 levels. Saturation Hue sample(x,y)=(R,G,B)-> map to (h,s)->Quantized->Histogram

7. Create the histogram Create the face model to get the skin color histogram Saturation Hue

8. Backprojection of a chromatic histogram Saturation Hue Saturation Hue Model Test 1 1.which bin 2.get value 2

9. Backprojection of a chromatic histogram Algorithm: Step 1: b x,y =M h(Cxy), Step 2: Convolving b x,y with a blurring mask Where h(C xy )= the bin corresponding to C xy, M I = the histogram of the Model with ith bin.

10. More example

11. Face tracking algorithm How to find a face in the initial frame Face region lies within a color range Face region Historgram for each region

12. Face tracking algorithm Compute an average of the face historgrams Face model histogram F (100 faces histogram)

13. Face tracking algorithm 1 2 3 Steps 1. Backproject 2. Binarized & CC 3. Search a ellipse

14. Finding an Ellipse An Ellipse which best fits the connected component is computed.

15. Least-Squares Curve Fitting Length of major (a) and minor (b) axis: Proof: computer and robot vision I, page 623

16. Finding an Ellipse The golden ratio of ellipse is picked up.

17. Tracking the face region Step 1: a new face model histogram F is constructed from the detected face. More precise face model, because tracking the same face. Step 2: Backprojected onto the next frame. Step 3: An elliptical mask is used for searching No ellipse finding,saving the computation cost why

18. Tracking the face region Step 4: compute the sum of the values of all pixels within the elliptical mask. Step 5: return maximum response location

19. Tracking the face region Prevent the searching cost for (left->right) & ( top -> down) Using motion information : (X i+1, Y i+1 )=(2X i - X i-1, 2Y i - Y i-1 ) X i-1,Y i-1 Xi,Yi (X i+1, Y i+1 )

20. Experements UperSPARC RISC with 60MHz, 64 MB Real-time processing 7 frames/sec (160x120) 3.5 frames/sec (240x180)

22. Face tracking (small face)

23. Face tracking (large face) error

25. Face tracking results using skip factor 5 change error

26. change error

27. Conclusion A histogram backprojection only needs a simple replacement operation Insensitive to small deformation and occlusion Because we use color information Two feature are used Face shape & chromatic

28. Conclusion It cannot handle non-forward faces Because we use a ellipse model to find a face. Zoom-in and Zoom-out We fixed the ellipse size due to reducing the computing cost.

29. Analysis 因為 Tracking algorithm 在第一張人臉抓 取後, 即 update face model histogram, => 所以第一張的人臉一定要抓的準確, 否則將造成一系列的錯誤 Face color histogram 是假設大家的膚色 類似, 但是若是 testing 有黑人, 白人, 則會 造成 histogram 分佈加大, 使得 backprojected 圖形更難處理.