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Modris Greitans, Arturs Kadikis, Rihards Fuksis Institute of Electronics and Computer Science Dzerbenes 14, Riga, Latvia

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Presentation on theme: "Modris Greitans, Arturs Kadikis, Rihards Fuksis Institute of Electronics and Computer Science Dzerbenes 14, Riga, Latvia"— Presentation transcript:

1 Modris Greitans, Arturs Kadikis, Rihards Fuksis Institute of Electronics and Computer Science Dzerbenes 14, Riga, Latvia Biohashing and Fusion of Palmprint and Palm Vein Biometric Data Rihards Fuksis International Conference on Hand-based Biometrics International Conference on Hand-based Biometrics November 17-18, Hong Kong

2 Rihards Fuksis International Conference on Hand-based Biometrics Motivation Multimodal Palm Biometrics Provides: Easy enrolment Unique parameters Hard to falsify

3 Rihards Fuksis International Conference on Hand-based Biometrics Image Acquisition (I) White LEDs In visible light spectrum using white LEDs

4 Rihards Fuksis International Conference on Hand-based Biometrics Image Acquisition (II) IR LEDs In infrared light spectrum using IR LEDs

5 Rihards Fuksis International Conference on Hand-based Biometrics Image processing (I) Cross section of the ridge Cross section of the vessels

6 Rihards Fuksis International Conference on Hand-based Biometrics 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 ridgeCross section of the vessels For further information: M.Greitans, M.Pudzs, R.Fuksis. Object Analysis in Images Using Complex 2d Matched Filters, Proceedings of the IEEE Region 8 Conference EUROCON Saint–Petersburg, Russia, May, 2009., pp

7 Rihards Fuksis International Conference on Hand-based Biometrics Image processing (III) Feature extraction Most significant vectors are extracted to describe the object. The result is a data set of 64 vectors (256 bytes) Filtering resultVector set

8 Rihards Fuksis International Conference on Hand-based Biometrics Vector set AVector set B Vector set from the database Acquired vector set Raw biometric data comparison

9 Rihards Fuksis International Conference on Hand-based Biometrics Vector comparison Magnitudes:

10 Rihards Fuksis International Conference on Hand-based Biometrics Vector comparison Magnitudes: Angles:

11 Rihards Fuksis International Conference on Hand-based Biometrics Vector comparison Magnitudes: Angles: Distance:

12 Rihards Fuksis International Conference on Hand-based Biometrics Vector comparison Magnitudes: Angles: Distance: Dot product

13 Rihards Fuksis International Conference on Hand-based Biometrics Vector set comparison Similarity of two vector sets: Similarity index is normalized so that S(A,B) is in the [0;1] Similarity index of two vectors:

14 Rihards Fuksis International Conference on Hand-based Biometrics Security of raw biometric data usage It is unsecure to use raw biometric data Therefore encryption must be introduced Raw biometric data Encrypted data

15 Rihards Fuksis International Conference on Hand-based Biometrics Inner product Biohash CMF Palm image PixelsVectors Vector Set (u,v) dv du u1u1 v1v1 du 1 dv 1 1st vector uRuR vRvR du R dv R... R-th vector Inner product... Token Thresholding Random number matrix 101 Biocode Data vector consists of 4R components... Biocode consists of 4R bits

16 Rihards Fuksis International Conference on Hand-based Biometrics Biohash Advancements(I) Filtered palm vein image

17 Rihards Fuksis International Conference on Hand-based Biometrics Biohash Advancements(I) Filtered palm vein image Extracted vector magnitudes

18 Rihards Fuksis International Conference on Hand-based Biometrics Biohash Advancements(I) Filtered palm vein image Extracted vector magnitudesMost intensive vector labeling

19 Rihards Fuksis International Conference on Hand-based Biometrics Biohash Advancements(I) Filtered palm vein image Extracted vector magnitudesMost intensive vector labeling u1u1 v1v1 du 1 dv 1 uRuR vRvR du R dv R... Data vector Most intensive vector information

20 Rihards Fuksis International Conference on Hand-based Biometrics Biohash Advancements(I) Filtered palm vein image Extracted vector magnitudesMost intensive vector labeling u1u1 v1v1 du 1 dv 1 uRuR vRvR du R dv R... Data vector + Most intensive vector information New Data vector

21 Rihards Fuksis International Conference on Hand-based Biometrics Biohash Advancements(II) Person 1; Biocode No.1 Person 1; Biocode No.2 Person 1; Biocode No.3 Person 1; Biocode No 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 Dot product Random number matrix Data vector Thresholding Capture this value Calculate the distance to the threshold

22 Rihards Fuksis International Conference on Hand-based Biometrics Biohash Advancements(II) Bit #1Bit #2Bit # Person 1; Biocode No.1 Person 1; Biocode No.2 Person 1; Biocode No.3 Person 1; Biocode No.4 Distance to the threshold Bit #1 Bit #2 Bit #3 If the distance to the threshold value is greater, the resulting bit most likely will not change between one persons biocodes...

23 Rihards Fuksis International Conference on Hand-based Biometrics Biohash Advancements(II) Distance to the threshold Bits Distance to the threshold Sort bits into groups

24 Rihards Fuksis International Conference on Hand-based Biometrics Biohash Advancements(II) Distance to the threshold Distance to the threshold Sort bits in every group in ascending order

25 Rihards Fuksis International Conference on Hand-based Biometrics Biohash Advancements(II) Distance to the threshold 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 Rihards Fuksis International Conference on Hand-based Biometrics Biocode comparison = 4 mistakes Similarity: l – length of the biocode D h – Hamming distance

27 Rihards Fuksis International Conference on Hand-based Biometrics Database evaluation Two databases; 500 images from 50 persons 5 images in IR and 5 in visible light spectrum Palm VeinsPalmprintsFused data Mean StDev Palm VeinsPalmprintsFused data Mean StDev Biohash test results [EER] Proposed Biohash test results [EER] Palm VeinsPalm PrintsFused data EER [%] Raw biometric data comparison results[EER]

28 Rihards Fuksis International Conference on Hand-based Biometrics 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 Rihards Fuksis International Conference on Hand-based Biometrics This presentation was supported by ERAF funding under the agreement No.2010/0309/2DP/ /10/APIA/VIA/012


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