1. Group 11 Sam Mazin & Priti Balchandani
Face-Detection using Maximal Rejection Classification and Color Techniques
Group 11
Sam Mazin & Priti Balchandani

2. Motivation Michael Elad’s presentation on rejection-based techniques
Felt a linear classifier (MRC) would be fastest and thus most practical in real-world applications

3. High-level Design Maximal Rejection Classifier (MRC) Color Rejection
Morphological Processing
FACES

4. Maximal Rejection Training: Background Backgrounds d1 Faces (Convex)θ

5. Not last θ: Project with θn+1
Maximal Rejection
Classifying:
Background
d1n ?
MRC Projection
15x15 Test Block
Last θ  Face
Not last θ: Project with θn+1
d2n
Very fast θn

6. Color Rejection
MRC was not the greatest on its own; many false positives remained
Tried RGB segmentation  not great
Y-Cb-Cr space showed promise
Decided to take mean Cb and Cr values of all 15x15 blocks (ignored Y to avoid intensity bias)

7. Color Rejection using FLD

8. Optimal Cb-Cr FLD Projection
Projection Density: p(w)
Faces
Backgrounds w
W*=max(FLD_proj(Faces))

9. Morphological Processing
Maximal Rejection Classifier (MRC)
Color Rejection
Morphological Processing
FACES

10. Morphological Processing
Combined 3 resolution levels into one
Dilated and performed centroid search to get rid of repeated face detections

11. Finally…

12. Desperate times… Monday night, 10pm
Still had 5-6 false positives popping up
Decided to implement the “Look Down Method”
Reran tests: made good scores better, but bad scores worse

13. Conclusion
MRC is fast but not 100% reliable (probably due to lack of data)
Color rejection helped significantly, Cb-Cr good means of classification
Morphological processing necessary for repeated detections
Spent too much time tweaking the MRC