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Automatic Iris Segmentation Using Active Near Infra Red Lighting Carlos Morimoto Thiago Santos Adriano Saturno Laboratory of Technologies for Interaction (LaTIn) Institute of Mathematics and Statistics (IME) University of S ao Paulo (USP)

  1. Automatic Iris Segmentation Using Active Near Infra Red Lighting Carlos Morimoto Thiago Santos Adriano Saturno Laboratory of Technologies for Interaction (LaTIn) Institute of Mathematics and Statistics (IME) University of S˜ ao Paulo (USP) hitoshi,thsant,saturno@ime.usp.br SIBGRAPI 2005, Natal - RN - Brazil www.ime.usp.br/ ∼ thsant/sibgrapi05 (IME) Iris Segmentation using NIR SIBGRAPI’05 1 / 22

  2. Teaser We present here a technique for iris segmentation that uses infra-red light in the image acquisition step. The pupil is identified and regions of iris occluded by eyelids are removed. The system can work in low cost adapted hardware. www.ime.usp.br/ ∼ thsant/sibgrapi05 (IME) Iris Segmentation using NIR SIBGRAPI’05 2 / 22

  3. Outline Introduction 1 Acquisition with NIR light 2 Segmentation: pupil, iris and eyelids 3 Implementation and results 4 Conclusion 5 www.ime.usp.br/ ∼ thsant/sibgrapi05 (IME) Iris Segmentation using NIR SIBGRAPI’05 3 / 22

  4. Introduction Iris as an identification method Unique pattern per person Twins don’t present the same pattern Phenotypical feature Two identical irises: 1 chance in 10 78 www.ime.usp.br/ ∼ thsant/sibgrapi05 (IME) Iris Segmentation using NIR SIBGRAPI’05 4 / 22

  5. Introduction Iris as an identification method Unique pattern per person Twins don’t present the same pattern Phenotypical feature Two identical irises: 1 chance in 10 78 Stable along life Structure: 3 rd ¯ to 8 th ¯ month of gestation Pigment changes: until the 1 st ¯ year of life www.ime.usp.br/ ∼ thsant/sibgrapi05 (IME) Iris Segmentation using NIR SIBGRAPI’05 4 / 22

  6. Introduction Iris as an identification method Unique pattern per person Twins don’t present the same pattern Phenotypical feature Two identical irises: 1 chance in 10 78 Stable along life Structure: 3 rd ¯ to 8 th ¯ month of gestation Pigment changes: until the 1 st ¯ year of life Iris Recognition Technology (IRT) Pilot ATMs in USA and England Authentication in European airports Commercial systems available: Evermedia LG Panasonic... www.ime.usp.br/ ∼ thsant/sibgrapi05 (IME) Iris Segmentation using NIR SIBGRAPI’05 4 / 22

  7. Introduction Iris as an identification method Unique pattern per person Twins don’t present the same pattern Phenotypical feature Two identical irises: 1 chance in 10 78 Stable along life Structure: 3 rd ¯ to 8 th ¯ month of gestation Pigment changes: until the 1 st ¯ year of life Iris Recognition Technology (IRT) Pilot ATMs in USA and England Authentication in European airports Commercial systems available: Evermedia LG Panasonic... IRT considered mature for biometry www.ime.usp.br/ ∼ thsant/sibgrapi05 (IME) Iris Segmentation using NIR SIBGRAPI’05 4 / 22

  8. Introduction Problems with IRT adoption Training to use the system positioning calibration www.ime.usp.br/ ∼ thsant/sibgrapi05 (IME) Iris Segmentation using NIR SIBGRAPI’05 5 / 22

  9. Introduction Problems with IRT adoption Training to use the system positioning calibration Difficult to accomplish using low-cost equipment: fixed focus micro cameras web-cams www.ime.usp.br/ ∼ thsant/sibgrapi05 (IME) Iris Segmentation using NIR SIBGRAPI’05 5 / 22

  10. Introduction Problems with IRT adoption Training to use the system positioning calibration Difficult to accomplish using low-cost equipment: fixed focus micro cameras web-cams Acquisition problems: eyelids occlusion specular reflection images out of focus www.ime.usp.br/ ∼ thsant/sibgrapi05 (IME) Iris Segmentation using NIR SIBGRAPI’05 5 / 22

  11. Introduction Problems with IRT adoption Training to use the system positioning calibration Difficult to accomplish using low-cost equipment: fixed focus micro cameras web-cams Acquisition problems: eyelids occlusion specular reflection images out of focus Need a robust, efficient, easy to use and low-cost system for iris segmentation. www.ime.usp.br/ ∼ thsant/sibgrapi05 (IME) Iris Segmentation using NIR SIBGRAPI’05 5 / 22

  12. Introduction Simple model for iris We would like to segment iris identifying the pupil as a circle, the iris as a circle or its outer border - the limbus - and the eyelids as elliptical segments. www.ime.usp.br/ ∼ thsant/sibgrapi05 (IME) Iris Segmentation using NIR SIBGRAPI’05 6 / 22

  13. Introduction Simple model for iris We would like to segment iris identifying the pupil as a circle, the iris as a circle or its outer border - the limbus - and the eyelids as elliptical segments. The eyelids position is arbitrary www.ime.usp.br/ ∼ thsant/sibgrapi05 (IME) Iris Segmentation using NIR SIBGRAPI’05 6 / 22

  14. Acquisition Camera NIR light sources position L 1 and L 2 turned on alternately www.ime.usp.br/ ∼ thsant/sibgrapi05 (IME) Iris Segmentation using NIR SIBGRAPI’05 7 / 22

  15. Acquisition Camera NIR light sources position L 1 and L 2 turned on alternately L 1 → “red eye” effect: bright pupil www.ime.usp.br/ ∼ thsant/sibgrapi05 (IME) Iris Segmentation using NIR SIBGRAPI’05 7 / 22

  16. Acquisition System’s geometry Near Infra Red light (NIR): 700-900nm. Practically invisible to human eye www.ime.usp.br/ ∼ thsant/sibgrapi05 (IME) Iris Segmentation using NIR SIBGRAPI’05 8 / 22

  17. Acquisition System’s geometry Near Infra Red light (NIR): 700-900nm. Practically invisible to human eye Light source L near to projection center C : www.ime.usp.br/ ∼ thsant/sibgrapi05 (IME) Iris Segmentation using NIR SIBGRAPI’05 8 / 22

  18. Acquisition System’s geometry Near Infra Red light (NIR): 700-900nm. Practically invisible to human eye Light source L near to projection center C : C , L and the reflection R ( glint ) are approximately collinears. www.ime.usp.br/ ∼ thsant/sibgrapi05 (IME) Iris Segmentation using NIR SIBGRAPI’05 8 / 22

  19. Acquisition System’s geometry Near Infra Red light (NIR): 700-900nm. Practically invisible to human eye Light source L near to projection center C : C , L and the reflection R ( glint ) are approximately collinears. R near the pupil center P if user is looking to the camera. www.ime.usp.br/ ∼ thsant/sibgrapi05 (IME) Iris Segmentation using NIR SIBGRAPI’05 8 / 22

  20. Segmentation System processing flow www.ime.usp.br/ ∼ thsant/sibgrapi05 (IME) Iris Segmentation using NIR SIBGRAPI’05 9 / 22

  21. Segmentation Pupil Outline Introduction 1 Acquisition with NIR light 2 Segmentation: pupil, iris and eyelids 3 Pupil segmentation Limbus and eyelids segmentation Implementation and results 4 Conclusion 5 www.ime.usp.br/ ∼ thsant/sibgrapi05 (IME) Iris Segmentation using NIR SIBGRAPI’05 10 / 22

  22. Segmentation Pupil Pupil segmentation L 1 produces a bright pupil image Small brightness changes out pupil www.ime.usp.br/ ∼ thsant/sibgrapi05 (IME) Iris Segmentation using NIR SIBGRAPI’05 11 / 22

  23. Segmentation Pupil Pupil segmentation L 1 produces a bright pupil image Small brightness changes out pupil L 2 produces an image with a dark pupil www.ime.usp.br/ ∼ thsant/sibgrapi05 (IME) Iris Segmentation using NIR SIBGRAPI’05 11 / 22

  24. Segmentation Pupil Pupil segmentation L 1 produces a bright pupil image Small brightness changes out pupil L 2 produces an image with a dark pupil Images subtraction gets pupil’s pixels www.ime.usp.br/ ∼ thsant/sibgrapi05 (IME) Iris Segmentation using NIR SIBGRAPI’05 11 / 22

  25. Segmentation Pupil Pupil segmentation L 1 produces a bright pupil image Small brightness changes out pupil L 2 produces an image with a dark pupil Images subtraction gets pupil’s pixels Circle fitting is used to segment the pupil www.ime.usp.br/ ∼ thsant/sibgrapi05 (IME) Iris Segmentation using NIR SIBGRAPI’05 11 / 22

  26. Segmentation Pupil Pupil segmentation L 1 produces a bright pupil image Small brightness changes out pupil L 2 produces an image with a dark pupil Images subtraction gets pupil’s pixels Circle fitting is used to segment the pupil User looking to the camera → glint is over the pupil www.ime.usp.br/ ∼ thsant/sibgrapi05 (IME) Iris Segmentation using NIR SIBGRAPI’05 11 / 22

  27. Segmentation Pupil Pupil segmentation www.ime.usp.br/ ∼ thsant/sibgrapi05 (IME) Iris Segmentation using NIR SIBGRAPI’05 11 / 22

  28. Segmentation Limbus and eyelid Outline Introduction 1 Acquisition with NIR light 2 Segmentation: pupil, iris and eyelids 3 Pupil segmentation Limbus and eyelids segmentation Implementation and results 4 Conclusion 5 www.ime.usp.br/ ∼ thsant/sibgrapi05 (IME) Iris Segmentation using NIR SIBGRAPI’05 12 / 22

  29. Segmentation Limbus and eyelid Limbus segmentation Limbus orientation Sclera is brighter than iris Gradient taken at limbus points to sclera www.ime.usp.br/ ∼ thsant/sibgrapi05 (IME) Iris Segmentation using NIR SIBGRAPI’05 13 / 22

  30. Segmentation Limbus and eyelid Limbus segmentation Limbus orientation Sclera is brighter than iris Gradient taken at limbus points to sclera Sobel operator used to get vertical edges − 1 0 1 − 2 0 2 − 1 0 1 www.ime.usp.br/ ∼ thsant/sibgrapi05 (IME) Iris Segmentation using NIR SIBGRAPI’05 13 / 22

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