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Biohashing and Fusion of Palmprint and Palm Vein Biometric Data

Biohashing and Fusion of Palmprint and Palm Vein Biometric Data. Modris Greitans , Arturs Kadikis , Rihards Fuksis Institute of Electronics and Computer Science Dzerbenes 14, Riga, Latvia e-mail: R ihards . F uksis @ edi . lv. International Conference on Hand-based Biometrics

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Biohashing and Fusion of Palmprint and Palm Vein Biometric Data

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  1. Biohashing and Fusion of Palmprint and Palm Vein Biometric Data ModrisGreitans, Arturs Kadikis, RihardsFuksis Institute of Electronics and Computer Science Dzerbenes 14, Riga, Latvia e-mail: Rihards.Fuksis@edi.lv International Conference on Hand-based Biometrics November17-18, Hong Kong Rihards Fuksis International Conference on Hand-based Biometrics

  2. Motivation Multimodal Palm Biometrics • Provides: • Easy enrolment • Unique parameters • Hard to falsify

  3. Image Acquisition (I) White LEDs In visible light spectrum using white LEDs

  4. Image Acquisition (II) IR LEDs In infrared light spectrum using IR LEDs

  5. Image processing (I) Cross section of the ridge Cross section of the vessels

  6. Image processing (II) Complex 2D Matched Filtering: • Based on the matched filtering • Improved processing speed • Obtains vectors: magnitude – matching rate; angle - orientation in the image Cross section of the ridge Cross section of the vessels Forfurtherinformation: M.Greitans, M.Pudzs, R.Fuksis. „Object Analysis in Images Using Complex 2d MatchedFilters”, Proceedings of the IEEE Region 8 Conference EUROCON 2009. Saint–Petersburg, Russia, May, 2009., pp. 1392-1397.

  7. Image processing (III) Feature extraction Filtering result Vector set Most significant vectors are extracted to describe the object. The result is a data set of 64 vectors (256 bytes)

  8. Raw biometric data comparison Vector set A Vector set B Acquired vector set Vector set from the database

  9. Vectorcomparison Magnitudes:

  10. Vector comparison Magnitudes: Angles:

  11. Vector comparison Magnitudes: Angles: Distance:

  12. Vector comparison Magnitudes: Angles: Distance: Dot product

  13. Vectorset comparison Similarity index of two vectors: Similarity of two vector sets: Similarity index is normalized so that S(A,B) is in the [0;1]

  14. Security of raw biometric data usage • It is unsecure to use raw biometric data • Therefore encryption must be introduced Encrypted data Raw biometric data

  15. Biohash du Pixels Vectors CMF Vector Set dv (u,v) Palm image 1st vector ... Inner product Inner product Inner product Token ... R-th vector Random number matrix ... Data vector consists of 4R components ... Thresholding ... Biocode consists of 4R bits Biocode 1 0 1

  16. Biohash Advancements(I) Filtered palm vein image

  17. Biohash Advancements(I) Extracted vector magnitudes Filtered palm vein image

  18. Biohash Advancements(I) Extracted vector magnitudes Most intensive vector labeling Filtered palm vein image

  19. Biohash Advancements(I) Extracted vector magnitudes Most intensive vector labeling Filtered palm vein image Data vector Most intensive vector information + ...

  20. Biohash Advancements(I) Extracted vector magnitudes Most intensive vector labeling Filtered palm vein image Data vector Most intensive vector information + ... New Data vector

  21. Biohash Advancements(II) By looking at the values before the thresholding in Biohash algorithm, we can obtain the information about the distance from threshold value for each of the bits in biocodes Random number matrix Dot product Data vector Capture this value Calculate the distance to the threshold Thresholding

  22. Biohash Advancements(II) If the distance to the threshold value is greater, the resulting bit most likely will not change between one person’s biocodes Distance to the threshold ... Bit #1 Bit #2 Bit #3

  23. Biohash Advancements(II) Distance to the threshold Bits Sort bits into groups Distance to the threshold 4 3 2

  24. Biohash Advancements(II) Distance to the threshold 4 3 2 Sort bits in every group in ascending order Distance to the threshold 4 3 2

  25. Biohash Advancements(II) Distance to the threshold 4 3 2 What we obtain is the indexes of the most “stable” bits in descending order. When comparing two biocodes this information is used to calculate weights for the errors of the bits by using exp or other function Weight function

  26. Biocode comparison = 4 mistakes l – length of the biocode Dh – Hamming distance Similarity:

  27. Database evaluation • Two databases; 500 images from 50 persons • 5 images in IR and 5 in visible light spectrum Raw biometric data comparison results[EER] Biohash test results [EER] Proposed Biohash test results [EER]

  28. Conclusions • Complex 2D Matched Filtering approach speeds up the feature extraction procedure. • Biohashing with proposed advancements can be used as a method for securing the biometric data with similar or better precision as raw biometric data comparison gives • Future work: Tests on larger databases and evaluation of other biometric encryption methods

  29. Thank you! Questions? This presentation was supported by ERAF funding under the agreement No.2010/0309/2DP/2.1.1.2.0/10/APIA/VIA/012

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