LDP Local Directional Pattern & LDN Local Directional Number Pattern

1. LDP Local Directional Pattern &LDNLocal Directional Number Pattern 报告人：黄倩颖

2. 内容 两种局部编码模式构造描述子 LDP Local Directional Pattern LDN Local Directional Number Pattern 对Local Binary Pattern (LBP)的改良

3. Descriptor geometric-feature-based appearance-based

4. Part One 作者简介 文章结构 方法概述 讲解提纲 • LBP方法回顾 • LDP的创新点 • LDP的鲁棒性 • LDP的旋转不变性 • 实验 • 结论

5. 作者简介 Local Directional Pattern (LDP) – A Robust Image Descriptor for Object Recognition TaskeedJabid, Md. HasanulKabir, OksamChae Department of Computer Engineering Kyung Hee University, Republic of Korea 2010 Seventh IEEE International Conference on Advanced Video and Signal Based Surveillance TaskeedJabid Human Computer Interaction, Computer Vision, Object Recognition Local Directional Pattern (LDP) for face recognition International Conference Consumer Electronics (ICCE), 2010 Cited by 44

6. 文章结构 • Introduction • LDP image descriptor • Local Binary Pattern (LBP) • Local Directional Pattern (LDP) • Robustness of LDP • Rotation invariant LDP • LDP Descriptor • Texture classification using LDP descriptor • Face recognition using LDP descriptor • Conclusions

7. Abstract LDP( Local Directional Pattern) is a local feature descriptor for describing local image feature. • Though LBP is robust to monotonic illumination change but it is sensitive to non-monotonic illumination variation and also shows poor performance in the presence of random noise • A LDP feature is obtained by computing the edge response values in all eight directions at each pixel position and generating a code from the relative strength magnitude. Each bit of code sequence is determined by considering a local neighborhood hence becomes robust in noisy situation.

8. Part One 作者简介 文章结构 方法概述 讲解提纲 • LBP方法回顾 • LDP的创新点 • LDP的鲁棒性 • LDP的旋转不变性 • 实验 • 结论

9. 讲解提纲 • LBP方法回顾 • LDP的创新点 • LDP的鲁棒性 • LDP的旋转不变性 • 实验 • 结论

10. Local Binary Pattern (LBP) Original LBP 26 < 500 Threshold 50 （0 0 1 1 1 0 0 0）2 = 56

11. Local Directional Pattern (LDP) Kirsch masks North M3 M2 M1 North-West North- East M0 M4 West East M7 M6 M5 South- West South- East South

12. Computing… Kirsch masks 19 LDPk k=3 LDP Binary Code = 00010011 LDP Decimal Code= 19

13. Robustness of LDP noise &non-monotonic illumination changes LBP = 00111000 LDP = 00010011 LBP = 00101000 LDP = 00010011

14. Rotation invariant LDP Rotation Invariant LDP Code = 00110001

15. LDP Descriptor Accumulating the occurrence of LDP feature

16. Experiments Texture Classification using LDP histogram Primary pictures from Brodatz texture album: (a) Bark, (b) Brick, (c) Bubbles, (d) Grass, (e) Leather, (f) Pigskin, (g) Raffia, (h) Sand, (i) Straw, (j) Water, (k) Weave, (l) Wood and (m) Wool

17. Experiments Texture Classification using LDP histogram

18. Experiments Extracted rotation invariant LDP features of each pixel of the image then combined to generate rotation invariant image descriptor using LDP histogram following equation.

19. Experiment Results The accuracy of the method Results

20. Face recognition using LDP descriptor Database FERET (a) faset, used as a gallery set, contains frontal images of 1,196 people. (b) fbset (1,195 images) with an alternative facial expression than in thefaphotograph. (c) fc set (194 images) taken under different lighting conditions. (d) dup I set (722 images) taken later in time. (e) dup II set (234 images) subset of the dup I set containing images that were taken at least a year after the corresponding gallery image.

21. Face recognition using LDP descriptor Classification using LDP histogram Template matching

22. Experiment Results

23. Part Two 作者简介 文章结构 方法概述 讲解提纲 • LBP LDP缺点 • LDN 三个关键点 • 人脸描述 • 实验 • 结论及未来工作

24. 作者简介 Local Directional Number Pattern for Face Analysis: Face and Expression Recognition Adin Ramirez Rivera,Student Member, IEEE, Jorge Rojas Castillo,Student Member, IEEE, and Oksam Chae,Member, IEEE IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 22, NO. 5, MAY 2013 Cited by 2 | Year 2012 | Adin Ramirez Rivera Image Processing, Computer Vision Content-Aware Dark Image Enhancement through Channel Division IEEE Transactions on Image Processing 21 (9), 3967-3980 Cited by 9 | Year 2012

25. 文章结构 • Introduction • Local Directional Number Pattern • Difference With Previous Work • Coding Scheme • Compass Masks • Face description • Face recognition • Conclusions

26. Abstract A novel local feature descriptor LDN encodes the directionalinformation of the face’s textures in a compact way, producing a more discriminative code than current methods

27. Part Two 作者简介 文章结构 方法概述 讲解提纲 • LBP LDP缺点 • LDN 三个关键点 • 人脸描述 • 实验 • 结论及未来工作

28. 讲解提纲 • LBP LDP缺点 • LDN 三个关键点 • 人脸描述 • 实验 • 结论及未来工作

29. LBP The method discards most of theinformation in the neighborhood. • It limits the accuracy of the method • It makes the methodvery sensitive to noise • Moreover, these drawbacks are moreevident for bigger neighborhoods

30. Directional (LDiP) & Derivative (LDeP) Miss some directional information (the responses’ sign) by treating all directions equally • Sensitive to illumination changes and noise, as the bits in the code will flip and the code will represent a totally different characteristic

31. Key points of LDN Direction number Sign information gradient information LBP LDN 6-bit

32. Key points of LDN gradient information Sign information Direction number LDN 6-bit

33. Coding Scheme Direction number - - + + Sign information

34. Coding Scheme

35. gradient information Compass Masks Two kinds of masks Kirsch masks derivative-Gaussian mask

36. Compass Masks Kirsch masks North M3 M2 M1 North-West North- East M0 M4 West East M7 M6 M5 South- West South- East South

37. Compass Masks derivative-Gaussian mask • Compute code in gradient space • Therefore, use Gaussian smoothing to stabilize the code in presence of noise Generate a compass mask,{M0σ,...,M7σ}, by rotating Mσ, 45°apart, in eight different directions

38. Compass Masks derivative-Gaussian mask

39. Face Descriptor Histogram LH & MLH

40. Face Descriptor Two kinds of descriptor Code in LH Code in MLH must be

41. Face Recognition Chi-Square dissimilarity measure

42. Face recognition using LDP descriptor Database FERET (a) faset, used as a gallery set, contains frontal images of 1,196 people. (b) fbset (1,195 images) with an alternative facial expression than in thefaphotograph. (c) fc set (194 images) taken under different lighting conditions. (d) dup I set (722 images) taken later in time. (e) dup II set (234 images) subset of the dup I set containing images that were taken at least a year after the corresponding gallery image.

43. Experiment Results Face recognition accuracy small neighborhoods (3×3, 5×5, 7×7) medium neighborhoods (5×5, 7×7, 9×9) large neighborhoods (7×7, 9×9, 11×11)

44. Experiment Results With white Gaussian noise Noise Evaluation

45. Conclusion • Combination of different sizes (small, medium and large) gives better recognition rates for certain conditions. • Evaluated LDN under expression, time lapse and illumination variations, and found that it is reliable and robust throughout all these conditions.

46. 总结及未来工作 • 如何选择一个描述子 • 长度 • 描述精度 • 抗噪能力 • 计算强度 • 如何设计一个描述子 • 舍弃冗余的信息 • 整合多种信息来源 • 信息压缩

47. Thank you!