The document summarizes a research paper on DeepFace, a face recognition system that closes the gap to human-level performance through 3D pose normalization. It first detects faces, then aligns faces through 2D alignment, 3D alignment involving fitting a 3D model, and frontalization. Representations are extracted and used for face verification, achieving human-level accuracy.

1. DeepFace: closing the gap to human-level
performance in face verification
-- 3D pose normalization
CVPR 2014
2014/09/23
ked

2. Authors
 Y. Taigman, M. Yang, M’A Ranzato, L. Wolf

3. Flow chart
 Face alignment
 2D alignment
 3D alignment
 Frontalization
 Representation
 Verification

4. Discussion about
 Face alignment
 2D alignment
 3D alignment
 Frontalization
 Representation
 Verification
Face detection

5. Face alignment
 Face alignment
 2D alignment
 3D alignment
 Frontalization
Face detection Cropping Feature alignment 3D model
3D triangle 2D triangle Normalization

6. Face alignment
 Face alignment
 2D alignment
 3D alignment
 Frontalization
Feature alignment
3D alignment
Get affine transformation
3D model
Frontalization
Draw the 3D model using
texture of the affine transformation
Feature alignment
3D model

7. Solve affine transformation
x y z 1 0 0 0 0
0 0 0 0 x y z 1
Transformation
8 x 1 (affine)
x
y
Vert_2D
(#fea x 2) x 1
Vert_3D
(#fea x 2) x 8
 Solve by least square method

8. Warping method – not work -.-||

9. Implementation – labels in 3D

10. Implementation mapping

11. Thx.