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Iris-based human verification system A research prototype

IWSSIP 2009. Iris-based human verification system A research prototype. Gorazd Vrček, Peter Peer Computer Vision Laboratory Faculty of Computer and Information Science, University of Ljubljana Ljubljana, Slovenia. Chalkida, June 19 2009. Roadmap. Verification, biometry, iris?

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Iris-based human verification system A research prototype

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  1. IWSSIP 2009 Iris-based human verification system A research prototype Gorazd Vrček, Peter Peer Computer Vision Laboratory Faculty of Computer and Information Science, University of Ljubljana Ljubljana, Slovenia Chalkida, June 19 2009

  2. Roadmap • Verification, biometry, iris? • System architecture • Results • Conclusion

  3. Verification vs. Identification

  4. Biometric Systems

  5. What is Iris? Iris

  6. Outline of the System • Segmentation • Normalization • Feature extraction • Iris comparison

  7. Segmentation • Input image? • ROI? • Problems (noise)? • Segmentation goal? • Start...

  8. Segmentation Getting information about the pupil: • Pupil edge

  9. Segmentation Getting information about the pupil: • Center • Radius • indexXleft • Xz • coarse center • indexYbottom • indexXright • Cz • indexYup • Yz

  10. Segmentation Getting information about the pupil (outer edge): • Image smoothing • Image illumination • Outer iris edge points detection • Generating iris mask

  11. Normalization • Based on Dougman’s homogeneous rubber sheet • With the center in the center of the pupil

  12. Feature Extraction • Gabor filter (2D Gabor wavelet) • Image convolution with it • The phase transformation used to convert the angles into iris template

  13. Iris Comparison • Comparison of two iris bit templates • Considering iris mask • Shift the bits and calculate again • Use the minimal Hamming distance

  14. Results • The comparison within the class provides the comparison of seven images of a person among themselves • The comparison between classesprovides the comparison of one iris image of a person with one of all other persons

  15. Person Verification • Result: positive/negative • Threshold for positive decision is set to HD≤0.427 value 0.427 gives FAR 0%, FRR 11.584%

  16. Conclusion • Research prototype → good results • Comparison with ICE 2006 results (FAR=0.1%): • To improve: segmentation optimization, noise detection • To upgrade: integrate iris capturing sensor

  17. Questions?

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