1. Recovery from Occlusion in Deep Feature Space for Face Recognition
Feng Cen

2. Motivation Deep convolutional neural networks:
Outperform human vision for face verification on LFW database
Fail to handle contiguous occlusion
Sparse representation classifier
Classical method for face images with occlusion
Image space or linear feature space
Difficult to deal with pose variations, facial expressions, and illumination changes etc.
D: training dictionary

3. Observation

5. Assumption

6. Algorithm
Residual:

7. Algorithm Dimension reduction with PCA
Normalization of the dictionary atom
Normalization of the residual with the l2 -norm of gallery coding coefficients

8. Experiments: AR Database
Parameters
Auxiliary
dictionary

9. Experiments: AR Database
Auxiliary dictionary generation

10. Experiments: AR Database
Performance

11. Experiments: AR Database
A single training sample per person

12. Experiments: FERET database
Training: 150 subjects, non-occlusion ‘ba’, ‘bj’, ‘bk’
Testing: 150 subjects, block occlusion
Auxiliary dictionary: other 44 subjects

13. Time comsumption Less than 0.4s per image – Intel i7 CPU
Dictionary coding: <2ms
CNNs : <0.4s without GPU acceleration

14. Conclusion
Real time
Robust to illumination changes, facial expressions, pose variations and contiguous occlusions
A single training sample per person

15. Thank you! Q&A