1. 電機四 B00901013 李舜仁

2. Outline Introduction Motivation Algorithms Future work F ace H allucination-Outline 1 1

3. Outline Introduction Motivation Algorithms Future work F ace H allucination-Outline 2 2

4. Hallucination ？ F ace H allucination-Introduction 3 3 Introduction

5. Face Hallucination F ace H allucination-Introduction 4 4 Face image super-resolution Low-resolution (LR) images Higher-resolution images

6. Super-resolution Enhance the resolution of an image. F ace H allucination-Introduction 5 5

7. Face hallucination employs typical face priors with strong cohesion to face domain concept. Ex. domain knowledge of the position of eyes, nose, mouth, etc. F ace H allucination-Introduction 6 6 Face Hallucination vs. Super-resolution

8. Outline Introduction Motivation Algorithms Future work F ace H allucination-Outline 7 7

9. Motivation F ace H allucination-Motivation 8 8 visual effect face recognition Why face hallucination data compression......

10. Face Recognition Problems: 1. Cheap surveillance camera 2. Long distance F ace H allucination-Motivation 9 9

11. Outline Introduction Motivation Algorithms Future work F ace H allucination-Outline 10

12. Algorithms Simplest: Interpolation Recent years: Learning from data training data: Y(HR) Y(LR) input: X(LR)target output: X(HR) F ace H allucination-Algorithms 11

13. Face hallucination based on Bayes theorem Super-resolution from multiple views using learnt image models Face Hallucination via Sparse Coding Face Hallucination by Eigentransformation Face hallucination based on MCA …… Introduction of methods based on co-occurrence model F ace H allucination-Algorithms 12 Algorithms

14. co-occurrence model F ace H allucination-Algorithms 13

15. Hallucinating face by position-patch Position patch: (Use domain knowledge) F ace H allucination-Algorithms 14 X. Ma, J. Zhang, and C. Qi, Pattern Recognition, 2010.

16. Position-patch based face hallucination using convex optimization C. Jung, L. Jiao, B. Liu, and M. Gong, Signal Processing Letters, 2011. F ace H allucination-Algorithms 15

17. Coupled-layer neighbor embedding for surveillance face hallucination J. Jiang, R. Hu, L. Chen, Z. Han, T. Lu, and J. Chen, in Proc. IEEE ICIP, 2013. F ace H allucination-Algorithms 16

18. Hallucinating face by eigen-transformation X. Wang and X. Tang, IEEE Trans. on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 2005. F ace H allucination-Algorithms 17

19. An example of experimental results F ace H allucination-Algorithms 18

20. F ace H allucination-Algorithms 19 An example of experimental results

21. PSNR MAX = 255 (8 bit) F ace H allucination-Algorithms 20

22. Outline Introduction Motivation Algorithms Future work F ace H allucination-Outline 21

23. Future work Problems Sensitiveness of misalignment Unconstrained condition Pose F ace H allucination-Future work 22

24. Unconstrained condition In reality, there are… expressions, illuminations, occlusions, etc. F ace H allucination-Future work 23

25. Occlusion F ace H allucination-Future work 24

26. Occlusion F ace H allucination-Future work 25

27. Pose F ace H allucination-Future work 26

28. Multiview Face Hallucination F ace H allucination-Future work 27

29. Multiview Face Hallucination F ace H allucination-Future work 28

30. 3D model F ace H allucination-Future work 29

31. Result F ace H allucination-Future work 30

32. Thank you !