1. Hand Recognition using Geometric Classifiers
Yaroslav Bulatov
Sachin Jambawalikar
Piyush Kumar
Saurabh Sethia
12/9/2018

2. Why hand recognition? Biometric = nice Cheap Privacy concerns
12/9/2018

3. Previous Work Jain, Ross and Pankanti, 1999 Jain and Duta, 1999
Sanchez-Reillo et.al., 2000
Oden et.al., 2001
12/9/2018

4. Methodology Acquire hand images Extract geometric features
Classify hands based on the extracted feature vector
12/9/2018

5. Data Acquisition Use Scanner Flexible Placement
No pegs or lighting setup
12/9/2018

6. Extracting Features
Finger lengths, widths, maximum inscribed circles in critical areas.
12/9/2018

7. Stages of Extraction Thresholding Smoothing Boundary Finding
Curvature Graph
Distance Transform
12/9/2018

8. Thresholding
12/9/2018

9. Thresholding
12/9/2018

10. Thresholding
12/9/2018

11. Thresholding Method Convert to CIELAB
Use Adaptive Thresholding on B channel
12/9/2018

12. CIEL*A*B
12/9/2018

13. Adaptive Thresholding
12/9/2018

14. 12/9/2018

15. Smoothing Minkowski Smoothing
Use round structuring element to perform erosion followed by dilation.
12/9/2018

16. Before Smoothing
12/9/2018

17. After Smoothing
12/9/2018

18. Boundary Finding Find rightmost point Follow boundary pixels CCW
At each boundary pixel, scan directions CW, and pick the first available
12/9/2018

19. Boundary Finding
12/9/2018

20. Curvature Finding
Need to locate feature points (ie fingertips, creases between fingers)
Look at curvature graph
12/9/2018

21. Curvature Finding
12/9/2018

22. Curvature Finding
12/9/2018

23. Curvature Finding
12/9/2018

24. Distance Transform
Even after smoothing, some boundary noise present, due to different hand placements
Need feature not sensitive to this noise
Maximum inscribed circle is one such feature
12/9/2018

25. Distance Transform
Assigns distance to the boundary to each of the point
Exact EDT computed in linear time using algorithm by Maurer.
12/9/2018

26. Distance Transform
12/9/2018

27. Distance Transform
The point with the highest value in the EDT is the center of inscribed circle
The largest EDT value is the radius of largest inscribed circle.
12/9/2018

28. Distance Transform
Inscribe circles into fingers by cutting off the finger in question
12/9/2018

29. 12/9/2018

30. Classification Extract 30 features from each hand image.
Given a query hand image, identify which person it belong to
12/9/2018

31. Nearest Box Classifier
Finds the bounding box of each
training set.
Given a query feature vector, classifies it to the nearest box.
12/9/2018

32. Minimum Enclosing Ball Classifier (MEB)
After 1d, 2d, 3d, …, high d..
What next?
12/9/2018

33. Minimum Enclosing Ball Classifier (MEB)
1-center in ∞ dimensions!!!
12/9/2018

34. 1-Center in 2002 Badoiu, Har-Peled, Indyk 2002 Ben-Hur et.al. 2002
Badoiu, Clarkson 2002
Kumar, Mitchell, Yildirim 2002
Har-Peled, Varadarajan 2002
12/9/2018

35. 2-Class Classification
Given :
Two sets of points
and
determine if belongs to P or Q
12/9/2018

36. 2-Class Classification
12/9/2018

37. Gaussian Kernel
Map P, Q to F using the Gaussian kernel. F
12/9/2018

38. Classification Algorithm
Compute MEB of
dim(F )
Here cannot be computed explicitly.
Use centers of MEB( ), MEB( ) to determine a separating hyperplane in F
12/9/2018

39. 1-center in ∞ dimensions
12/9/2018

40. A Projection of H
12/9/2018

41. A Projection of H
12/9/2018

42. A Projection of H
12/9/2018

43. A Projection of H
12/9/2018

44. Hand Outline Recognition
Used Voronoi diagrams in F to extend the idea of 2 class classification.
Feature space lived in 30 dimensions.
Outlier removal using modified Mahalanobis distance.
Compares well with Support Vector Machines for our application (Beats it!)
12/9/2018

45. Verification
Nearest Box
MEB
12/9/2018

46. Identification
Nearest Box
MEB
12/9/2018

47. Previous Experimental results
Jain et. al. do verification using 500 images. FAR=2%, FRR=15%
Jain, Duta do verification using 353 images. FAR=2%, FRR=3.5%
Sanchez-Reillo et.al. do verification and classification using 200 images. Error rates: Classification 3%, Verification 10%.
Oden et.al. do verification and classification. Error rates: Verification 10%, Classification 15%.
12/9/2018

48. Misclassification Rates
Training Set Size
Nearest Box
MEB
SVM 3
2.81
1.53
4.77 4
1.74
.94
.93 5
1.72
.39
1.93
12/9/2018

49. Future Work Testing our classifier on standard datasets.
Using weighted Voronoi diagram for multiclass classification.
Practical provable outlier removal in F
12/9/2018