Biohashing and Fusion of Palmprint and Palm Vein
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: Rihards. Fuksis@edi. lv International Conference on Hand-based Biometrics November 17 -18, Hong Kong Rihards Fuksis International Conference on Hand-based Biometrics
Motivation Multimodal Palm Biometrics ØProvides: ØEasy enrolment ØUnique parameters ØHard to falsify Rihards Fuksis International Conference on Hand-based Biometrics
Image Acquisition (I) White LEDs In visible light spectrum using white LEDs Rihards Fuksis International Conference on Hand-based Biometrics
Image Acquisition (II) IR LEDs In infrared light spectrum using IR LEDs Rihards Fuksis International Conference on Hand-based Biometrics
Image processing (I) Cross section of the ridge Cross section of the vessels Rihards Fuksis International Conference on Hand-based Biometrics
Image processing (II) Complex 2 D 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 For further information: M. Greitans, M. Pudzs, R. Fuksis. „Object Analysis in Images Using Complex 2 d Matched Filters”, Proceedings of the IEEE Region 8 Conference EUROCON 2009. Saint–Petersburg, Russia, May, 2009. , pp. 1392 -1397. Rihards Fuksis International Conference on Hand-based Biometrics
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) Rihards Fuksis International Conference on Hand-based Biometrics
Raw biometric data comparison Vector set A Vector set B Vector set from the database Acquired vector set Rihards Fuksis International Conference on Hand-based Biometrics
Vector comparison Magnitudes: Rihards Fuksis International Conference on Hand-based Biometrics
Vector comparison Magnitudes: Angles: Rihards Fuksis International Conference on Hand-based Biometrics
Vector comparison Magnitudes: Angles: Distance: Rihards Fuksis International Conference on Hand-based Biometrics
Vector comparison Magnitudes: Angles: Distance: Dot product Rihards Fuksis International Conference on Hand-based Biometrics
Vector set 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] 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 10 11 00 01 01 11 10 10 00 10 Encrypted data Raw biometric data Rihards Fuksis International Conference on Hand-based Biometrics
Biohash du Pixels CMF Vectors (u, v) Palm image Inner product . . . 1 st vector u 1 v 1 R-th vector u. R v. R Inner product . . . Random number matrix du 1 dv 1. . . Token dv Vector Set du. R dv. R Data vector consists of 4 R components . . . Thresholding 1 0 Rihards Fuksis . . . 1 Biocode consists of 4 R bits International Conference on Hand-based Biometrics
Biohash Advancements(I) Filtered palm vein image Rihards Fuksis International Conference on Hand-based Biometrics
Biohash Advancements(I) Filtered palm vein image 76 27 187 49 83 163 87 44 52 85 146 83 41 57 87 51 Extracted vector magnitudes Rihards Fuksis International Conference on Hand-based Biometrics
Biohash Advancements(I) Filtered palm vein image 76 27 187 49 0 0 1 0 83 163 87 44 0 1 0 0 52 85 146 83 0 0 1 0 41 57 87 51 0 0 Extracted vector magnitudes Rihards Fuksis Most intensive vector labeling International Conference on Hand-based Biometrics
Biohash Advancements(I) Filtered palm vein image 76 27 187 49 0 0 1 0 83 163 87 44 0 1 0 0 52 85 146 83 0 0 1 0 41 57 87 51 0 0 Extracted vector magnitudes Data vector u 1 v 1 du 1 dv 1. . . u. R Most intensive vector labeling Most intensive vector information v. R Rihards Fuksis du. R dv. R + 0 0 1 0 0 International Conference on Hand-based Biometrics 1. . . 0
Biohash Advancements(I) Filtered palm vein image 76 27 187 49 0 0 1 0 83 163 87 44 0 1 0 0 52 85 146 83 0 0 1 0 41 57 87 51 0 0 Extracted vector magnitudes Data vector u 1 v 1 du 1 dv 1. . . u. R Most intensive vector labeling Most intensive vector information v. R du. R dv. R + 0 0 1. . . 0 New Data vector Rihards Fuksis International Conference on Hand-based Biometrics
Biohash Advancements(II) Person 1; Biocode No. 1 1 1 0. . . Person 1; Biocode No. 2 1 0 1. . . Person 1; Biocode No. 3 1 1 0. . . Person 1; Biocode No. 4 1 1 1. . . By looking at the values before thresholding in Biohash algorithm, we can obtain the information about the distance from threshold value for each of the bits in biocodes 4 3 2 Random number matrix Data vector Dot product Capture this value Calculate the distance to the threshold Rihards Fuksis Thresholding International Conference on Hand-based Biometrics
Biohash Advancements(II) Bit #1 Bit #2 Bit #3 Person 1; Biocode No. 1 1 0. 61 1 0. 33 0 0. 04 . . . Person 1; Biocode No. 2 1 0. 47 0 0. 12 1 0. 15 . . . Person 1; Biocode No. 3 1 0. 59 1 0. 47 0 0. 18 . . . Person 1; Biocode No. 4 1 0. 46 1 0. 39 1 0. 14 . . . 4 2. 13 3 1. 19 2 0. 29 . . . Distance to the threshold Bit #1 Bit #2 Bit #3 Rihards Fuksis . . . If the distance to the threshold value is greater, the resulting bit most likely will not change between one person’s biocodes International Conference on Hand-based Biometrics
Biohash Advancements(II) Distance to the threshold Bits Sort bits into groups Distance to the threshold 4 Rihards Fuksis 3 2 International Conference on Hand-based Biometrics
Biohash Advancements(II) Distance to the threshold 4 3 2 Sort bits in every group in ascending order Distance to the threshold 4 Rihards Fuksis 3 2 International Conference on Hand-based Biometrics
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 Rihards Fuksis International Conference on Hand-based Biometrics
Biocode comparison = 4 mistakes Similarity: Rihards Fuksis l – length of the biocode Dh – Hamming distance 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 Raw biometric data comparison results[EER] EER [%] Palm Veins Palm Prints Fused data 0. 32 2. 79 0. 1 Biohash test results [EER] Palm Veins Palmprints Fused data Mean 14. 043 12. 073 6. 190 St. Dev 1. 152 1. 102 0. 803 Proposed Biohash test results [EER] Palm Veins Palmprints Fused data Mean 1. 073 0. 471 0 St. Dev 0. 304 0. 231 0 Rihards Fuksis International Conference on Hand-based Biometrics
Conclusions • Complex 2 D 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 Rihards Fuksis International Conference on Hand-based Biometrics
Thank you! Questions? This presentation was supported by ERAF funding under the agreement No. 2010/0309/2 DP/2. 1. 1. 2. 0/10/APIA/VIA/012 Rihards Fuksis International Conference on Hand-based Biometrics
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