1. A Convolutional Neural Network Cascade For Face Detection
CV lab
Chanmi you

2. A Convolutional Neural Network Cascade For Face Detection
H. Li, Z. Lin, X. Shen, J. Brandt, G. Hua, A Convolutional Neural Network Cascade for Face Detection, CVPR 2015

3. Contents
Testing Process
Training Process
Experiments
Conclusion

4. Testing process

5. 12-net 12-calibration-net 24-net 24-calibration-net 48-calibration-net
Apply 12-net
to obtain face/non-face classification
Calibrate each patches
+NMS
Input
24-net
Calibrated patches
Extract 12x12 patch from whole image
Face patches after 12-net
Apply 24-net
Face patches after 24-net
+NMS
24-calibration-net
48-calibration-net
48-net
Calibrate each patches
Calibrate each patches
+NMS
Apply 48-net
Face patches after 48-net
Calibrated patches
Output

6. 12-net (Detection net) : 12x12 detection window
For each detection windows,

7. 12-calibration-net CNN after 12-net for bounding box calibration
Given detection window (𝑥, 𝑦, 𝑤, ℎ)
Calibration patterns (𝑁=45)
For apply

8. 12-calibration-net (cont’d)
Take the average results of the patterns
After 12-calibration-net, Non-maximum suppression applied

9. 24-net (Detection net)
For multi-resolution, Fully-connected layer from 12-net is concatenated

10. 24-calibration-net
Similar with 12-calibration-net After 24-calibration-net, Non-maximum suppression applied

11. 48-net (Detection net)
For multi-resolution, Fully-connected layer from 24-net is concatenated. Relatively more complicated. After 48-net, Non-maximum suppression applied

12. 48-calibration-net
Relatively more complicated.

13. Training process Calibration nets Detection nets
For the 𝑛-th pattern [ 𝑠 𝑛 , 𝑥 𝑛 , 𝑦 𝑛 ], apply [ 1/𝑠 𝑛 , −𝑥 𝑛 , −𝑦 𝑛 ]
Detection nets
12-net
Resize all training faces into 12x12
Randomly sample 200,000 non-face patches from background images
Choose a threshold 𝑇 1 at 99 % recall rate
24-net
Resize all training faces into 24x24
Densely scan all background images
All detection windows with confidence score (after 12-net) larger than 𝑇 1 become negative training samples
Choose a threshold 𝑇 2 at 97 % recall rate
48-net
Resize all training faces into 48x48
Following same procedure.
Neg: 5800 background images
Pos: Annotated Facial Landmarks in the Wild(AFLW dataset)

14. Experiments
Annotated Faces in the Wild (AFW dataset)

15. Experiments (cont’d)
Face Detection Data Set and Benchmark (FDDB dataset)

16. Conclusion Face detection using CNN and cascade
Reject non-face regions quickly at low resolution
Process accurate detection at higher resolution
Calibration nets are introduced in the cascade to accelerate detection and improve bounding box quality