Master Thesis - Department of Computer Science

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Table 5.7: Combined performance (in Percentage Accuracy) with PIE and F PA databases for different decision fusion strategies. T estSet Sum Max Min Product DT DS PLDA PNLDA T est11 93.82 83.71 87.41 93.71 76.53 75.29 97.76 98.29 T est12 91.32 83.23 82.65 88.01 78.89 76.59 94.25 95.74 T est13 90.98 82.55 80.39 86.86 79.71 78.14 95.29 96.27 T est14 90.29 82.25 76.67 86.08 83.92 84.51 96.76 97.84 T est21 96.94 81.88 88.23 97.41 76.35 77.53 98.53 97.82 T est22 93.90 81.25 82.79 91.47 80.51 78.09 96.10 94.71 T est23 93.63 80.69 80.59 91.37 80.88 79.31 96.57 94.31 T est24 93.23 80.98 76.67 89.31 84.61 84.90 98.04 95.39 Table 5.8: Combined performance (in Percentage Accuracy) with PIE and F PB databases for different decision fusion strategies. T estSet Sum Max Min Product DT DS PLDA PNLDA T est11 90.59 88.94 79.76 89.00 78.18 78.29 93.00 93.47 T est12 84.41 85.66 68.38 77.72 74.12 75.07 86.32 85.22 T est13 81.17 85.29 62.35 73.14 69.02 68.73 80.68 80.29 T est14 87.65 83.73 62.45 73.82 75.49 76.67 88.92 88.92 T est21 90.76 87.29 79.88 88.41 79.23 78.82 94.71 93.41 T est22 87.21 85.37 69.12 80.96 75.44 76.18 89.85 86.84 T est23 84.41 84.90 62.25 76.86 70.20 70.29 86.27 81.86 T est24 89.31 84.02 62.45 76.57 77.35 78.43 92.65 89.80 114
LDA and LDA produce the same accuracy of 96.67%, whereas sum rule, the next best method in this case, gives 85.56% (see second last row of Table 5.10). 3. Face classifier is good while fingerprint classifier is bad: The performance of face and fingerprint classifier on T est1 of PIE and T est4 of F PB are 81.47% and 63.33% respectively. The combined performaces for this pair, using PLDA, PNLDA and sum rule are 88.92%, 88.92% and 87.65% (see 4 th row of Table 5.8). 4. Face classifier is bad while fingerprint classifier is good: The performance of face and fingerprint classifiers on T est2 of ORL and T est1 of F PA are 76.67% and 86.00% respectively. We obtained a maximum of 98.00% accuracy for both PLDA and PNLDA, as compared to 95.00% accuracy provided by sum rule (see 5 th row of Table 5.9). Table 5.9: Combined performance (in Percentage Accuracy) with ORL and F PA databases for different decision fusion strategies. T estSet Sum Max Min Product DT DS PLDA PNLDA T est11 95.33 84.67 90.00 95.67 88.00 87.00 98.00 98.00 T est12 92.50 85.00 82.52 89.58 87.08 88.75 96.67 97.08 T est13 91.67 85.00 80.00 87.22 89.44 90.56 97.22 96.67 T est14 91.11 84.44 76.67 86.67 92.77 92.77 97.22 96.67 T est21 95.00 76.67 87.00 93.00 85.67 88.33 98.00 98.00 T est22 90.42 76.67 82.50 88.33 84.17 85.83 96.25 95.83 T est23 89.44 76.67 80.00 86.67 87.22 88.33 95.56 95.00 T est24 87.78 76.67 76.67 84.44 90.56 90.00 95.00 95.56 In each of the cases described above, our proposed algorithm performs better than other methods. We have gained a percentage accuracy of 11.11% using both LDA and nonparametric LDA over their best competitor sum rule in test case 2, where we have combined two bad performing classifiers. In addition, one can observe (in general) that the performance of our proposed methods in the last two columns in Tables 5.7-5.10 are always better than any of the other approaches which do not use 115
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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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Page 163 and 164: Bibliography [1] FVC 2002 website.
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Page 169 and 170: [71] L. Lam and C. Y. Suen. Applica
Page 171 and 172: IEEE Tran. on Pattern Analysis and
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Master Thesis - Department of Computer Science

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