The document discusses face detection using the modified census transform. It presents existing works that use techniques like histogram equalization, unit variance, zero mean, local binary patterns, and linear classifiers. It then discusses the problem of illumination variance in object recognition and introduces the census transform, which captures local spatial structure. The modified census transform uses a 3x3 neighborhood and 511 kernel indices to generate weak classifiers for a boosting approach. Training involves accumulating AdaBoost and Winnow updates over multiple stages to classify faces and backgrounds.

1. Face Detection with the
Modified Census Transform
Bernhard Froba Andreas Ernst
presented by Hyoungjin Kim

2. existing works
• histogram equalization
• unit variance
• zero mean
• local binary pattern
• linear SNoW classifier

3. existing works
• histogram equalization
• unit variance(= unit standard deviation)
- divide all pixels by the standard deviation
• zero mean
• local binary pattern
• linear SNoW classifier

4. existing works
• histogram equalization
• unit variance
• zero mean(subtract mean from
all pixels)
• local binary pattern
• linear SNoW classifier

5. existing works
• histogram equalization
• unit variance
• zero mean
• local binary pattern
• linear SNoW classifier

6. existing works
• histogram equalization
• unit variance
• zero mean
• local binary pattern
• linear SNoW classifier

7. existing works
• histogram equalization
• unit variance
• zero mean
• local binary pattern
• linear SNoW classifier

8. problem domain
• Illumination variance is a big problem in
object recognition which usually requires a
costly compensation to classification
• Ordering information is robust to outliers
and invariant to monotonic intensity
distortions Ramin Zabih and John Woodfill

9. census transform Ramin Zabih and John Woodfill
usually transform computes
some summary of local
intensities.

10. census transform Ramin Zabih and John Woodfill
a summary of local spatial structure

11. census transform Ramin Zabih and John Woodfill
from John Woodfill and Brian Von Herzen

12. The modified census transform

13. Local structure patterns
• num of kernel index
2^9-1 = 511
• used 3X3 neighborhood

14. How to generate
weak classifier
• The single feature weak classifier
at position x with the lowest
boosting error e and with regard
to maximum number of feature
positions allowed is chosen in
boosting loop.
•

15. How to generate
weak classifier

16. Strong classifier

17. training of stage classifier
AdaBoost Winnow update
• first stage: 20 lookup-table operations have
to be accumulated(Low complexity!)

18. Training the last stage
background face
The two sets of weight-tables {hx-face} and {hx-
background} are trained using an iterative procedure
param for Winnov update
threshold : T
promotion : A
demotion : B