1. Statistical Learning of Multi-View Face Detection
Microsoft Research Asia
Stan Li, Long Zhu, Zhen Qiu Zhang, Andrew Blake, Hong Jiang Zhang, Harry Shum
Presented by Derek Hoiem

2. Overview Viola-Jones AdaBoost FloatBoost Approach
Multi-View Face Detection
FloatBoost Results
FloatBoost vs. AdaBoost
FloatBoost Discussion

3. Face Detection Overview
Evaluate windows at all locations in many scales
Classifier
Non-Object
Object

4. Viola-Jones AdaBoost Weak classifiers formed out of simple features
In sequential stages, features are selected and weak classifiers trained with emphasis on misclassified examples
Integral images and a cascaded classifier allow real-time face detection

5. Viola-Jones Features
For a 24 x 24 image: 190,800 semi-continuous features
Computed in constant time using integral image
Weak classifiers consist of filter response threshold
Vertical
Horizontal
On-Off-On
Diagonal

6. Integral Image y = I8 – I7– I6 + I5+ I4 – I3 – I2 + I1 I( x1, y1 )

7. Cascade of Classifiers
Input Signal (Image Window)
40%
Stage 1
1 Weak Classifier
60%
40%
Stage 2
5 Weak Classifiers
Class 2 (Non-Face)
60%
99.999% …
0.001%
40%
Stage N
1200 Weak Classifiers
Class 1 (Face)

8. Viola-Jones AdaBoost Algorithm
Strong classifier formed from weak classifiers:
At each stage, new weak classifier chosen to minimize bound on classification error (confidence weighted):
This gives the form for our weak classifier:

9. Viola-Jones AdaBoost Algorithm

10. Viola-Jones AdaBoost Pros and Cons
Very fast
Moderately high accuracy
Simple implementation/concept
Greedy search through feature space
Highly constrained features
Very high training time

11. FloatBoost Weak classifiers formed out of simple features
In each stage, the weak classifier that reduces error most is added
In each stage, if any previously added classifier contributes to error reduction less than the latest addition, this classifier is removed
Result is a smaller feature set with same classification accuracy

12. MS FloatBoost Features
For a 20 x 20 image: over 290,000 features (~500K ?)
Computed in constant time using integral image
Weak classifiers consist of filter response threshold
Microsoft
Viola-Jones

13. FloatBoost Algorithm

14. FloatBoost Weak Classifiers
Can be portrayed as density estimation on single variables using average shifted histograms with weighted examples
Each weak classifier is a 2-bin histogram from weighted examples
Weights serve to eliminate overcounting due to dependent variables
Strong classifier is a combination of estimated weighted PDFs for selected features

15. Multi-View Face Detection Head Rotations
In-Plane Rotations: -45 to 45 degrees
Out of Plane Rotation: -90 to 90 degrees
Moderate Nodding

16. Multi-View Face Detection Detector Pyramid

17. Multi-View Face Detection Merging Results
Frontal
Right Side
Left Side

18. Multi-View Face Detection Summary
Simple, rectangular features used
FloatBoost selects and trains weak classifiers
A cascade of strong classifiers makes up the overall detector
A coarse-to-fine evaluation is used to efficiently find a broad range of out-of-plane rotated faces

19. Results: Frontal (MIT+CMU)
FloatBoost/AdaBoost/RBK
20x20 images
3000 original faces, 6000 total
100,000 non-faces
Schneiderman
FloatBoost
FloatBoost vs. Adaboost

20. Results: MS Adaboost vs. Viola-Jones Adaboost
More flexible features
Confidence-weighted AdaBoost
Smaller image size

21. Results: Profile
No Quantitative Results!!!

22. FloatBoost vs. AdaBoost
FloatBoost finds a more potent set of weak classifiers through a less greedy search
FloatBoost results in a faster, more accurate classifier
FloatBoost requires longer training times (5 times longer)

23. FloatBoost vs. AdaBoost
1 Strong Classifier, 4000 objects, 4000 non-objects, 99.5% fixed detection

24. FloatBoost: Pros Very Fast Detection (5 fps multi-view)
Fairly High Accuracy
Simple Implementation

25. FloatBoost: Cons Very long training time Not highest accuracy
Does it work well for non-frontal faces and other objects?