Paper presentation: The relative distance of key point based iris recognition
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This document presents a paper on iris recognition using key point-based relative distances as features. It introduces iris image preprocessing such as segmentation, normalization, and Gabor filtering. Features are extracted by detecting key points within filtered image blocks and calculating relative distances between points. The method is tested on two databases for verification and identification, achieving error rates of 0.008% and 0.0015%. While the method has advantages like rotation invariance and compact features, the authors note it may be more suitable for medium security applications than high-security ones.
Paper presentation: The relative distance of key point based iris recognition
- 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. OUTLINE The Relative Distance of Key Point Based Iris Recognition Iris Image Preprocessing Features Extracting Experimental Results Verification Identification Conclusion
- 3. OUTLINE The Relative Distance of Key Point Based Iris Recognition Iris Image Preprocessing Features Extracting Experimental Results Verification Identification Conclusion
- 5. IMAGE PREPROCESSING (2/6) Longest Chord (x,y) (xp,yp)
- 6. IMAGE PREPROCESSING (3/6) Inner Boundary Outer Boundary
- 8. IMAGE PREPROCESSING (5/6) r 1 0 0 π Φ
- 9. IMAGE PREPROCESSING (6/6) Φ 0 π r 64 pixels 1 256 pixels
- 10. OUTLINE The Relative Distance of Key Point Based Iris Recognition Iris Image Preprocessing Features Extracting Experimental Results Verification Identification Conclusion
- 11. GABOR FILTER (1/7) Original Image Gabor Filter Result Image
- 13. GABOR FILTER (3/7) For Multi-channels: θ = 0, 45, 90, 135 T = α = β = 4, 8, 16, 32
- 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 Farrokhnia T = α = β = 4, 8, 16, 32 Multichannel Texture Analysis Using Localized Spatial Filters Alan Conrad Bovik Marianna Clark Wilson S. Geisler
- 15. GABOR FILTER (4/7) φ = 0, 90 θ = 0, 45, 90, 135 Unsupervised Texture Segmentation Using Gabor Filters Anil K. Jain Farshid Farrokhnia T = α = β = 4, 8, 16, 32 Multichannel Texture Analysis Using Localized Spatial Filters Alan Conrad Bovik Marianna Clark Wilson S. Geisler
- 16. GABOR FILTER (6/7) φ = 0, 90 2 Parts θ = 0, 45, 90, 135 16 Channels T = α = β = 4, 8, 16, 32
- 17. GABOR FILTER (7/7) φ = 0, 90 2 Parts θ = 0, 45, 90, 135 32 Filters 16 Channels T = α = β = 4, 8, 16, 32
- 18. FILTER EXAMPLE (1/3) T = 4 ;θ = 0 T = 8 ; θ = 45 4 Channels T = 16 ; θ = 90 T = 32 ; θ = 135
- 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
- 21. KEY POINTS (1/4) 256 pixels 64 pixels 32 pixels 32 pixels To divide filtered image into 16 blocks
- 22. KEY POINTS (2/4) 256 pixels 64 pixels → obtain a key point: by here, m = 64
- 23. KEY POINTS (3/4) for each filtered image 16 key points
- 24. KEY POINTS (4/4) 32 filtered images exist 32 filters 16×32 = 512 key points
- 25. 32 blo RELATIVE DISTANCE (1/3) ck s 32 filtered images To obtain the center of key points in the jth blocks by:
- 26. RELATIVE DISTANCE (2/3) for some block j: Oj KPn Dj(n)
- 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. OUTLINE The Relative Distance of Key Point Based Iris Recognition Iris Image Preprocessing Features Extracting Experimental Results Verification Identification Conclusion
- 29. EXPERIMENTS Database Experiments Modes CASIA Verification 108×7 = 756 Identification 320×280 pixels private database 254×4 = 1016 768×568 pixels
- 30. OUTLINE The Relative Distance of Key Point Based Iris Recognition Iris Image Preprocessing Features Extracting Experimental Results Verification Identification Conclusion
- 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. NUMBER OF COMPARISONS (2/2) Small overlaps CASIA private database
- 33. ACCURACY ROC curve 0.008% 0.0015% CASIA private database
- 34. PARAMETER M control tradeoff accuracy speed small m lose feature noise sensitive large m many redundant features
- 35. OUTLINE The Relative Distance of Key Point Based Iris Recognition Iris Image Preprocessing Features Extracting Experimental Results Verification Identification Conclusion
- 36. IDENTIFICATION CASIA : 108×(3+4) private : 254×(3+1) Testing Training
- 37. OUTLINE The Relative Distance of Key Point Based Iris Recognition Iris Image Preprocessing Features Extracting Experimental Results Verification Identification Conclusion
- 38. MISMATCHING Lower resolution!! Eyelids obscuring!! Darkness!!
- 39. FEATURES (1/5) Good recognition rate (with a better choice of m) point number in a block
- 40. FEATURES (2/5) Can avoid influence of rotation transform
- 41. FEATURES (3/5) Feature dimensions is only 512
- 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. FEATURES (5/5) 0.008% 0.0015% CASIA private database
- 44. FINALLY This proposed method is more suitable for medium security such as those for civil access control (require a high match rate) Daugmen and Ma’s methods are more suitable for high security system such as military departments (require a low FAR)