WLBP: Weber local binary pattern for local image description

1. WLBP: Weber local binary pattern for local image description Fan Liu, Zhenmin Tang, Jinhui Tang, 报告人：陈 霞

2. 作者简介 Fan Liu • a Ph.D. candidate of School of Computer Engineering of Nanjing University of Science and Technology. • His research interests include computer vision, image processing and pattern recognition. • fanfanliu.njust@gmail.com Biography • He received his B.S. degree in network engineering from Nanjing University of Science and Technology (NUST) in June 2005.. • He is currently working towards the Ph.D. degree in computer application technology at Nanjing University of Science and Technology.

3. 作者简介 Jinhui Tang • a Professor of School of Computer Science and Technology, Nanjing University of Science and Technology. • His current research interests include large-scale multimedia search, social media analysis and computer vision.KeyLab of Intelligent Information Processing(IIP) • tangjh1981@acm.org Biography • He has over 60 journal and conference papers in these areas. • Dr. Tang is a recipient of the 2008 President Scholarship of Chinese Academy of Science, and a co- recipient of the Best Paper Award in ACM Multimedia 2007. He is a member of ACM, IEEE and IEEE-CS.

4. 文章出处 Publication • Neurocomputing, v 120（2013）, p 325-335 References • Jie Chen,ShiguangShan,ChuHe,etal.,WLD:a robust local image descriptor, IEEE Trans.PatternAnal.Mach.Intell.32(9)(2010)1705–1720. • A . K. Jain, Fundamentals of Digital Signal Processing, Prentice-Hall , Englewood Cliffs, NJ,1989.

5. Abstract We propose a local descriptor , called Weber Local Binary Pattern(WLBP), which effectively combines the advantages of WLD and LBP. WLBP consists of two components: differential excitation（差励）and LBP. By computing the two components, we obtain two images: differential excitation image and LBP image it is robust to time, facial expressions, lightings, pose and noise.

6. Motivation 论文主要贡献 1）使用Log算子计算差分激励。 2）用LBP替换WLD中的方向部分,因为方向只利用了中心像素点周围的四个像素点信息，LBP则利用了周围八个像素点，因此，LBP保留了更多的局部结构信息。

7. 文章结构 • Abstract • Introduction • Weber local binary pattern • Experiments for face recognition • Conclusion

8. 讲解提纲 • Weber’s Law • LBP • Differential Excitation(ξ) • WLBPDescriptor • 实验：WLBP应用于人脸识别 • 结论

9. 讲解提纲 • Weber’s Law • LBP • Differential Excitation(ξ) • WLBPDescriptor • 实验：WLBP应用于人脸识别 • 结论

10. Weber’s Law • 韦伯定律是表明心理量和物理量之间关系的定律。德国生理学家和心理学家韦伯(1795－1878)发现同一刺激差别量必须达到一定比例，才能引起差别感觉。这一比例是个常数，用公式表示： ΔI/I = k • ΔI：差别阈限 • I：标准刺激强度 • k：常数/韦伯分数

11. 讲解提纲 • Weber’s Law • LBP • Differential Excitation(ξ) • WLBPDescriptor • 实验：WLBP应用于人脸识别 • 结论

12. LBP

13. 讲解提纲 • Weber’s Law • LBP • Differential Excitation(ξ) • WLBPDescriptor • 实验：WLBP应用于人脸识别 • 结论

14. Differential Excitation(ξ) • 原来的方法

15. Differential Excitation(ξ) • 改进使用Log

16. Differential Excitation(ξ) • ΔI/I = k • set a constant K to segment the range of ξ into two classes: • high perception pattern [−K,K] • low perception pattern [−π/2,−K],[K, π/2]

17. 讲解提纲 • Weber’s Law • LBP • Differential Excitation(ξ) • WLBPDescriptor • 实验：WLBP应用于人脸识别 • 结论

18. WLBP Descriptor • For a given image, we compute the pattern value for every pixel by using the uniform LBP operator and the differential excitation of every pixel . • get two images: differential excitation image and LBP image. • we first construct the 2D histogram of the original image. • To enhance the discriminability,the2D histogram {WLBP(s,t)}is further encoded into1D histogram.

19. WLBP Descriptor

20. WLBP Descriptor Comparison with WLD WLD: • Differential excitation • Orientation LBP can extract more local structure information than Orientation. LBP makes full use of the nine pixel while Orientation operator only utilizes four pixels (x1, x3, x5, x7)

21. WLBP Descriptor By combining the differential excitation and LBP, WLBP not only holds the high discriminability but also obtains the robustness to noise and illumination.

22. 讲解提纲 • Weber’s Law • LBP • Differential Excitation(ξ) • WLBPDescriptor • 实验：WLBP应用于人脸识别 • 结论

23. 实 验 • FERET database • Includes1400 images of 200 individuals(each individual has seven images). • This subset involves variations in facial expression , illumination , and pose. • All experimental results are average value over ten runs

24. 实 验 • Evaluation without noise

25. 实 验 • Evaluation with noise

26. 实 验 • AR database • contains over 4000 color face images of 126 people (70 men and 56 women） • including frontal views of faces with different facial expressions, lighting conditions.

27. 实 验 • Experiments on the AR database

28. Conclusion • WLBP consists of two components: differential excitation and LBP. • WLBP is more discriminative than or equal to LBP. By combining the differential excitation and LBP, WLBP not only holds the high discriminability but also obtains the robustness to noise and illumination.

29. Conclusion • 不足 • the feature vector length of WLBP is obviously longer than that of LBP and WLD. • It slows down the recognition speed, especially for very large databases.

30. Conclusion • 未来工作 • In our future work , we will consider to reduce dimensionality of the feature vector and improve the computing efficiency. • In addition, future interest also lies in how to exploit the proposed descriptor for the application of object recognition.

31. Thank you!