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Paper presentation: The relative distance of key point based iris recognition

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Paper presentation: The relative distance of key point based iris recognition

1. 1. Paper Presentation: “The relative distance of key point based iris recognition” Li Yu David Zhang Kuanquan Wang ‘Grandma, do you mind if I do an iris recognition scan?’Rueshyna ● Jaiyalas May 23 2010
2. 2. OUTLINEThe Relative Distance of Key Point BasedIris Recognition Iris Image Preprocessing Features ExtractingExperimental Results Veriﬁcation IdentiﬁcationConclusion
3. 3. OUTLINEThe Relative Distance of Key Point BasedIris Recognition Iris Image Preprocessing Features ExtractingExperimental Results Veriﬁcation IdentiﬁcationConclusion
4. 4. IMAGE PREPROCESSING (1/6)
5. 5. IMAGE PREPROCESSING (2/6) Longest Chord (x,y) (xp,yp)
6. 6. IMAGE PREPROCESSING (3/6) Inner Boundary Outer Boundary
7. 7. IMAGE PREPROCESSING (4/6)
8. 8. IMAGE PREPROCESSING (5/6) r 1 0 0 π Φ
9. 9. IMAGE PREPROCESSING (6/6) Φ 0 π r 64 pixels 1 256 pixels
10. 10. OUTLINEThe Relative Distance of Key Point BasedIris Recognition Iris Image Preprocessing Features ExtractingExperimental Results Veriﬁcation IdentiﬁcationConclusion
11. 11. GABOR FILTER (1/7)Original Image Gabor Filter Result Image
12. 12. GABOR FILTER (2/7)
13. 13. GABOR FILTER (3/7)For Multi-channels:θ = 0, 45, 90, 135T = α = β = 4, 8, 16, 32
14. 14. GABOR FILTER (5/7) even-symmetric (real part)φ = 0, 90 odd-symmetric (imaginary part)θ = 0, 45, 90, 135 Unsupervised Texture Segmentation Using Gabor Filters Anil K. Jain Farshid FarrokhniaT = α = β = 4, 8, 16, 32 Multichannel Texture Analysis Using Localized Spatial Filters Alan Conrad Bovik Marianna Clark Wilson S. Geisler
15. 15. GABOR FILTER (4/7)φ = 0, 90θ = 0, 45, 90, 135 Unsupervised Texture Segmentation Using Gabor Filters Anil K. Jain Farshid FarrokhniaT = α = β = 4, 8, 16, 32 Multichannel Texture Analysis Using Localized Spatial Filters Alan Conrad Bovik Marianna Clark Wilson S. Geisler
16. 16. GABOR FILTER (6/7)φ = 0, 90 2 Partsθ = 0, 45, 90, 135 16 ChannelsT = α = β = 4, 8, 16, 32
17. 17. GABOR FILTER (7/7)φ = 0, 90 2 Partsθ = 0, 45, 90, 135 32 Filters 16 ChannelsT = α = β = 4, 8, 16, 32
18. 18. FILTER EXAMPLE (1/3) T = 4 ;θ = 0 T = 8 ; θ = 45 4 Channels T = 16 ; θ = 90 T = 32 ; θ = 135
19. 19. FILTER EXAMPLE (2/3) T = 4 ;θ = 0 T = 8 ; θ = 45 4 Even-Symmetric Filters T = 16 ; θ = 90 with φ = 0 T = 32 ; θ = 135
20. 20. FILTER EXAMPLE (3/3)
21. 21. KEY POINTS (1/4) 256 pixels64 pixels 32 pixels 32 pixels To divide ﬁltered image into 16 blocks
22. 22. KEY POINTS (2/4) 256 pixels64 pixels → obtain a key point: by here, m = 64
23. 23. KEY POINTS (3/4)for each ﬁltered image 16 key points
24. 24. KEY POINTS (4/4)32 ﬁltered images exist 32 ﬁlters 16×32 = 512 key points
25. 25. 32 blo RELATIVE DISTANCE (1/3) ck s32 ﬁltered images To obtain the center of key points in the jth blocks by:
26. 26. RELATIVE DISTANCE (2/3)for some block j: Oj KPn Dj(n)
27. 27. RELATIVE DISTANCE (3/3)32 ﬁltered images There are 32 D in 1st blocks There are 32 D in 2nd blocks 16 x 32 . . = 512 Distances . = 512 Features!! There are 32 D in 16th blocks
28. 28. OUTLINEThe Relative Distance of Key Point BasedIris Recognition Iris Image Preprocessing Features ExtractingExperimental Results Veriﬁcation IdentiﬁcationConclusion
29. 29. EXPERIMENTSDatabase Experiments Modes CASIA Veriﬁcation 108×7 = 756 Identiﬁcation 320×280 pixels private database 254×4 = 1016 768×568 pixels
30. 30. OUTLINEThe Relative Distance of Key Point BasedIris Recognition Iris Image Preprocessing Features ExtractingExperimental Results Veriﬁcation IdentiﬁcationConclusion
31. 31. NUMBER OF COMPARISONS (1/2) CASIA private database 570,780 total 1,031,240 total 2,268 intra-class 1524 intra-class 568,512 inter-class 1,029,716 inter-class
32. 32. NUMBER OF COMPARISONS (2/2) Small overlaps CASIA private database
33. 33. ACCURACYROC curve 0.008% 0.0015% CASIA private database
34. 34. PARAMETER Mcontrol tradeoff accuracy speedsmall m lose feature noise sensitivelarge m many redundant features
35. 35. OUTLINEThe Relative Distance of Key Point BasedIris Recognition Iris Image Preprocessing Features ExtractingExperimental Results Veriﬁcation IdentiﬁcationConclusion
36. 36. IDENTIFICATION CASIA : 108×(3+4) private : 254×(3+1) Testing Training
37. 37. OUTLINEThe Relative Distance of Key Point BasedIris Recognition Iris Image Preprocessing Features ExtractingExperimental Results Veriﬁcation IdentiﬁcationConclusion
38. 38. MISMATCHING Lower resolution!! Eyelids obscuring!! Darkness!!
39. 39. FEATURES (1/5)Good recognition rate (with a better choice of m) point number in a block
40. 40. FEATURES (2/5)Can avoid inﬂuence of rotation transform
41. 41. FEATURES (3/5)Feature dimensions is only 512
42. 42. FEATURES (4/5)Compared with Daugman’s and Ma’s methods integrating location and modality info. more powerful when: FAR > 0.008% (in CASIA) FAR > 0.0015% (in private database)
43. 43. FEATURES (5/5)0.008% 0.0015% CASIA private database
44. 44. FINALLYThis proposed method is more suitable formedium security such as those for civilaccess control (require a high match rate)Daugmen and Ma’s methods are moresuitable for high security system such asmilitary departments (require a low FAR)