1. End-to-End Facial Alignment and Recognition

2. Introduction
Increase in face recognition accuracy

3. End-to-End Facial Alignment and Recognition

4. Why do we need STN
Ideal Images
Real Images

5. SPN predicts the coefficients of an affine transformation
STN Architecture
SPN predicts the coefficients of an affine transformation

6. Trying different localization architectures

7. SPN predicts the coefficients of an affine transformation
STN Architecture
SPN predicts the coefficients of an affine transformation

8. Parameterized Sampling grid
The grid generator’s job is to output a parametrised sampling grid, which is a set of points where the input map should be sampled to produce the desired transformed output.
The column vector xin, yin consists in a set of indices that tell us where we should sample our input to obtain the desired transformed output.
Compute the pixel value in output image ,take the value in the input image at the right place

9. Spatial Transformer Network

10. Identity transformation

11. SPN predicts the coefficients of an affine transformation
STN Architecture
SPN predicts the coefficients of an affine transformation

12. Bilinear Interpolation

13. Differential Gradient

14. STN Result

15. STN Result

18. Recognition ResNet with 9 residual blocks
24 convolution layers in total
512 dimensional output feature vector

19. Results

21. End-to-End Spatial Transform Face Detection and Recognition

22. Architecture
Region feature transformation
Align the detected faces

23. Detection Similar to Faster R-CNN VGG-16 (pre-trained on Image-Net)
Region Proposal Network
ROI Pooling
Spatial Transformer Network

24. Faster RCNN

25. Region Proposal

26. ROI Pooling

27. Architecture
Region feature transformation
Align the detected faces

28. SPN predicts the coefficients of an affine transformation
STN Architecture
SPN predicts the coefficients of an affine transformation

29. Recognition Another STN is added before the recognition part ResNet:
ResNet with 9 residual blocks
24 convolution layers in total
512 dimensional output feature vector

30. Results

31. References
Jaderberg, Max, Karen Simonyan, and Andrew Zisserman. "Spatial transformer networks." Advances in Neural Information Processing Systems
Chi, Liying, Hongxin Zhang, and Mingxiu Chen. "End-To-End Face Detection and Recognition." arXiv preprint arXiv: (2017).