Master Thesis - Department of Computer Science

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A.6.3 Matching Score Computation Once the count of matched minutiae, NMM, is obtained, a matching score (represents the degree of match) is computed using one of the following formulas: MS = √ NMM , P ∗ Q (A.38) MS = 2NMM . (A.39) P + Q where P and Q are the number of minutiae features in fingerprints P and Q, used for matching. We also used a normalized score, which takes into account the closeness (in the Euclidean sense) of paired minutiae features. This is computed as: NMS = MS D NMM +1 . (A.40) where, D is the sum of all the Euclidean distances of the paired minutiae obtained after registration. A.7 Experimental Results The following databases are used for evaluating our fingerprint recognition system, • DBA consists of 10 subjects each having 8 samples (FVC 2002 Database [1]). • DBB consists of 10 subjects each having 8 samples (FVC 2004 Database [2]). Table A.1: Accuracy (in Percentage Accuracy) for DBA and DBB. Case DBA DBB Case A 88.75 69.00 Case B 90.00 70.00 The recognition performance for two databases are tabulated in Table A.1. The accuracies for each database is calculated for two different cases, where the matching score is calculated using : Case A Eqn. (A.40), and Case B Eqn. (A.38). Case A provides a stricter check than case B by taking Euclidean Distance (D) under consideration. In all cases, only one sample per subject is used for training and rest of the images are used for recognition. 142
Bibliography [1] FVC 2002 website. http://bias.csr.unibo.it/fvc2002. [2] FVC 2004 website. http://bias.csr.unibo.it/fvc2004. [3] ORL database. http://www.uk.research.att.com/facedatabase.html. [4] Oxford English Dictionary. Oxford Edition, 2004. [5] PIE feature point labels. http://www.hid.ri.cmu.edu/Hid/databases pieLabels.html. [6] C. Arcelli and G. S. D. Baja. A Width Independent Fast Thinning Algorithm. IEEE Tran. on Pattern Analysis and Machine Intelligence, 4(7):463–474, 1984. [7] D. H. Ballard. Generalizing the Hough Transform to Detect Arbitrary Shapes. Pattern Recognition, 3(2):110–122, 1981. [8] D. H. Ballard and C. M. Brown. Computer Vision. Englewood Cliffs, NJ: Prentice-Hall, 1982. [9] Robert J. Baron. Mechanisms of Human Facial Recognition. International Journal of Man-Machine Studies, 15(2):137–178, 1981. [10] A.M. Bazen and S. H. Gerez. Segmentation of Fingerprint Images. In Workshop on Circuits Systems and Signal Processing (ProRISC 2001), pages 276–280, 2001. [11] A.M. Bazen and S. H. Gerez. Systematic Methods for the Computation of the Directional Fields and Singular Points of Fingerprint. IEEE Tran. on Pattern Analysis and Machine Intelligence, 24(7):905–919, 2002. [12] Peter N. Belhumeur, Joao P. Hespanha, and David J. Kriegman. Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Tran. on Pattern Analysis and Machine Intelligence, 19(7):711–720, Jul. 1997. [13] J. Berry and D. A. Stoney. The History and Development of Fingerprinting (H. C. Lee and R. Gaensslen eds.). CRC Press, 2nd ed., pp. 1-40, Florida:, 2001.
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DEVELOPMENT OF EFFICIENT METHODS FO
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To My Parents, Sisters and Dearest
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Mirnalinee, Shreyasee, Lalit, Manis
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LDC Linear Discriminant Classifier
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3 An Efficient Method of Face Recog
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A.6 Minutiae Matching . . . . . . .
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4.1 Effect of increasing number of
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List of Figures 2.1 Summary of appr
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3.7 Area difference between genuine
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Abstract Biometrics is a rapidly ev
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CHAPTER 1 Introduction The issues a
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1.1.1 Applications Biometrics has b
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• Spoof Attacks: An impostor may
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• A new approach for multimodal b
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CHAPTER 2 Literature Review This re
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ange [23], infrared scanned [137] a
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u 2 x 2 u 1 x 1 (a) PCA basis (b) P
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PCA Projection Class 1 Class 2 LDA
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F DIFS DFFS F (a) (b) F 1 L Figure
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image A of m rows and n columns is
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faces of 40 subjects. • Others: A
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malizing the vector components, the
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Figure 2.5: A fingerprint image wit
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features (gradient coherence, inten
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Figure 2.7: Examples of minutiae; A
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according to the local orientation
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• Parallel Mode: This operational
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can address the problem of noisy se
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Prior to Matching Sensor Level Feat
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determined by logistic regression.
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Class-indifferent Methods • Decis
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3. The final DS soft vector is calc
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on three face databases and compare
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for a face. This was illustrated by
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Image Rows h(.) g(.) 2 2 Columns Fi
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in the accuracy of face recognition
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(a) (a1) (a2) (a3) (b) (b1) (b2) (b
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Genuine Impostor Scores Scores 1 0
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Error d1 = (1 − TZeroF RR) d2 = |
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The algorithm for obtaining the sub
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each subject, 42 samples (flashes f
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Table 3.3: Peak Recognition Accurac
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Table 3.5: Peak Recognition Accurac
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Table 3.7: Performance of Ekenel’
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Table 3.9: Performance of Ekenel’
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to our proposed method. Section 4.2
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plored both of the spaces to captur
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project only the class means in ran
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ΩNull and ΩRange represents the
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Thus, calculation of the QR factori
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4.3.3 Algorithm for Feature Fusion
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The architecture of our proposed me
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T R T S denoted by RVNull and RVNul
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x DNull DRange DP (x) DNull(x) DRan
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C 2 C 1 C 1 C 2 C X X 2 M X M X 2 1
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Master Thesis - Department of Computer Science

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