1. ECE738 Final Project Face Detection Baseline
Tanaphol Thaipanich

2. Objectives Face localization High detection rate & Low false alarm
Color Image with good lighting condition
Upright Frontal (Pose, Rotation and Occlusion)
Low / High resolution
High detection rate & Low false alarm
Fast performance (real-time)

3. Solution
Skin color map
Haar-like features with Boosted Cascade

4. Skin color map Douglas Chai and King N. Ngan (1999)
Success rate of 82%
Sanjay Kr. Singh, D. S. Chauhan, Mayank Vatsa and Richa Singh (2003)
RGB(97), YCbCr(99) and HIS(96)
Accuracy of 95.18%

5. Skin color map

6. Skin color map
The chrominance values in facial region are narrowly distributed
RCb = [77 127] and RCr = [ ]
Robust against different types of skin color
Brightness is nonuniform throughout facial region
Block size of 8x8
If SD <= 2 then it is unlikely to be facial region

7. Skin color map Density regulation Geometric Correction
Erode and dilate
Geometric Correction
Remove isolate noise in background

8. Experiments

9. Experiments

10. Haar-like features with Boosted Cascade
Over-complete Haar-like features
Fast feature extraction
Feature reduction – Adaboost
Fast decision – Attentional Cascade

11. Over-complete Haar-like features
Paul Viola and Michael Jones
Window 24 x 24
Rainer Lienhart and Jochen Maydt
More complex but yield better result

12. Over-complete Haar-like features
Total =
20736 features

13. Fast feature extraction
Fast feature computation  Integral Image
Integral Image at (x,y) = sum of all pixels above and to the left of (x,y)
Ex) D = 4+1-(2+3) = A+B+C+D+A – (A+B+A+C)

14. Adaboost
Combine small number of these features to form a strong classifier
Idea:
Each round of boosting select 1 feature  lowest classification error when perform alone
After each round, re-adjust weight of training samples  weight more on misclassified data
Create “Strong classifier” based on those features

15. Adaboost
Example of the selected features by Adaboost

16. Attentional cascade
Use simple classifier to reject the majority of sub-window before calling more complex classifier  Low false alarm
Stage 1
Stage 2
Stage N
Sub-Window
Reject Sub-Window

17. Experimental results Training set
Face database: AR, BioID, ORL and Yales  3871 face samples
Noface (opposing class) database: practically unlimited

18. Experimental results Training process: Extremely time-consuming
What they did:
Use 4916 face samples and 350 million (Oh! My god) noface samples (from 9544 images)
38 stages with over 6000 features
Detection rate = 95% and false alarm = 1 in 14084
Now, what we have:
Use 250 face samples and 250 noface samples (randomly select from a pool of samples)
3 stages with 50 features
Detection rate = 93.4% (miss 33 from 500)
False alarm = 5.4% (false detect 27 from 500)

19. Experimental results Improvement
Combine result from “Skin map detection” & speed-up using detection map (Multiple detection)

20. Experimental results

21. Experimental results

22. Tanaphol Thaipanich Thaipanich@wisc.edu
Thank you
Tanaphol Thaipanich