Master Thesis - Department of Computer Science

More documents

Recommendations

Info

lution face recognition [36] for Yale, PIE and ORL databases based on the successful subbands determined on testing set. From the results given in Table 3.7, we can observe that the process of decision fusion based on sum and product rules perform the best among all decision combination strategies proposed in [36]. However, in case of ORL, this strategy fails to improve upon that provided by the gray-level image using PCA. In the second case of experimentation, the successful subbands are selected using the validation set, as used in our proposed technique (see Table 3.1). Results are given in Table 3.8-3.9, in a similar manner, as in Table 3.6-3.7. We can notice that the over-tuning done in the first case offers apparently better results, by comparing the performances given in Table 3.7 (first case) and the corresponding top seven rows of Table 3.9 (second case). Table 3.6: Best performing and successful subbands for Ekenel’s multiresolution face recognition [36] for Yale, PIE and ORL databases determined on testing set. Yale PIE ORL Best Performing Subband V3 HA2 A2 A1, V2, V3 A1, H1, V1, D1 A1, A2, A3 Successful Subbands H2, V2, D2 Selected for Data, H3, V3, D3 Feature, Decision HA3, HH3, HV3, HD3 Fusion V A3, V H3, V V3, V D3 DA3, DH3, DV3, DD3 Hence, to provide a meaningful comparative study, which is also practically use- ful, we use the second approach (based on validation set) to compare the performance of our underlying subband face representation with that suggested in [36], using data, feature and decision level fusion. We do so with identical feature extraction and dis- tance metric, which is PCA and L2 norm in both cases. Image size is also kept as same for both cases. We use the same validation set to select the subbands performing equally good and better than original image for multiresolution face recognition. The 66
Table 3.7: Performance of Ekenel’s multiresolution face recognition [36] for Yale, PIE and ORL databases based on the successful subbands determined on testing set. Yale PIE ORL Methods PRA EER PRA EER PRA EER Original Face 81.67 7.78 58.85 17.05 80.83 3.33 Best Performing Subband 88.33 5.95 90.64 7.50 70.83 8.49 Data Fusion 76.67 16.67 34.46 28.21 69.17 7.07 Feature Fusion 73.33 20.00 23.91 32.42 71.67 7.68 Decision Fusion-Sum Rule 85.00 8.38 88.65 3.24 70.00 7.38 Decision Fusion-Product Rule 85.00 8.67 90.77 8.82 72.50 6.82 Decision Fusion-Max Rule 81.67 10.00 66.09 49.67 67.50 7.5 feature (subband) selection criterion is however different. Then those selected subbands are combined at data, feature and decision level, as described in multiresolution approach [36] to obtain the results in Table 3.9. For Yale database, original image with PCA and L2 norm gives 81.67%. Experi- mentation on subband selection for fusion shows that A1, A2 and A3 (see Table 3.8) perform same as A0 (original image) on validation set. So A1, A2 and A3 are combined at data, feature and decision level and the performance was observed on the testing set. Note that, any of the fusion strategies (Table 3.9) do not perform better than original image. It was also observed that A1 is the best performing subband on testing set for Yale database. In our approach, four criteria used for subband selection provide accuracies in the range [91.67%-95.00%] (refer to first row of Table 3.3) while decision fusion using three (sum, product and max) rules in Ekenel’s multiresolution face recognition provide a PRA of only 70% (Table 3.9). We are however able to obtain a 100% recognition in case of Yale database (see last row of Table 3.9), using fourth criterion C4 of subband selection and LDA (see second row of Table 3.3). For PIE database, we obtain comparable performance with Ekenel’s multiresolution technique. PRAs for the best performing subband (HA3), decision fusion for sum and product rules are 90.64%, 85.83% and 88.20% respectively, while our subject- 67
Page 1 and 2:
DEVELOPMENT OF EFFICIENT METHODS FO
Page 3 and 4:
To My Parents, Sisters and Dearest
Page 5 and 6:
Mirnalinee, Shreyasee, Lalit, Manis
Page 7 and 8:
LDC Linear Discriminant Classifier
Page 9 and 10:
3 An Efficient Method of Face Recog
Page 11 and 12:
A.6 Minutiae Matching . . . . . . .
Page 13 and 14:
4.1 Effect of increasing number of
Page 15 and 16:
List of Figures 2.1 Summary of appr
Page 17 and 18:
3.7 Area difference between genuine
Page 19 and 20:
Abstract Biometrics is a rapidly ev
Page 21 and 22:
CHAPTER 1 Introduction The issues a
Page 23 and 24:
1.1.1 Applications Biometrics has b
Page 25 and 26:
• Spoof Attacks: An impostor may
Page 27 and 28:
• A new approach for multimodal b
Page 29 and 30:
CHAPTER 2 Literature Review This re
Page 31 and 32:
ange [23], infrared scanned [137] a
Page 33 and 34:
u 2 x 2 u 1 x 1 (a) PCA basis (b) P
Page 35 and 36: PCA Projection Class 1 Class 2 LDA
Page 37 and 38: F DIFS DFFS F (a) (b) F 1 L Figure
Page 39 and 40: image A of m rows and n columns is
Page 41 and 42: faces of 40 subjects. • Others: A
Page 43 and 44: malizing the vector components, the
Page 45 and 46: Figure 2.5: A fingerprint image wit
Page 47 and 48: features (gradient coherence, inten
Page 49 and 50: Figure 2.7: Examples of minutiae; A
Page 51 and 52: according to the local orientation
Page 53 and 54: • Parallel Mode: This operational
Page 55 and 56: can address the problem of noisy se
Page 57 and 58: Prior to Matching Sensor Level Feat
Page 59 and 60: determined by logistic regression.
Page 61 and 62: Class-indifferent Methods • Decis
Page 63 and 64: 3. The final DS soft vector is calc
Page 65 and 66: on three face databases and compare
Page 67 and 68: for a face. This was illustrated by
Page 69 and 70: Image Rows h(.) g(.) 2 2 Columns Fi
Page 71 and 72: in the accuracy of face recognition
Page 73 and 74: (a) (a1) (a2) (a3) (b) (b1) (b2) (b
Page 75 and 76: Genuine Impostor Scores Scores 1 0
Page 77 and 78: Error d1 = (1 − TZeroF RR) d2 = |
Page 79 and 80: The algorithm for obtaining the sub
Page 81 and 82: each subject, 42 samples (flashes f
Page 83 and 84: Table 3.3: Peak Recognition Accurac
Page 85: Table 3.5: Peak Recognition Accurac
Page 89 and 90: Table 3.9: Performance of Ekenel’
Page 91 and 92: to our proposed method. Section 4.2
Page 93 and 94: plored both of the spaces to captur
Page 95 and 96: project only the class means in ran
Page 97 and 98: ΩNull and ΩRange represents the
Page 99 and 100: Thus, calculation of the QR factori
Page 101 and 102: 4.3.3 Algorithm for Feature Fusion
Page 103 and 104: The architecture of our proposed me
Page 105 and 106: T R T S denoted by RVNull and RVNul
Page 107 and 108: x DNull DRange DP (x) DNull(x) DRan
Page 109 and 110: C 2 C 1 C 1 C 2 C X X 2 M X M X 2 1
Page 111 and 112: iii) Project all class means onto n
Page 113 and 114: Table 4.1: Effect of increasing num
Page 115 and 116: Table 4.3: Effect of increasing num
Page 117 and 118: (Original face) (Null face) (Range
Page 119 and 120: Table 4.6: Performance of Dual Spac
Page 121 and 122: 5.1 Introduction In recent years, t
Page 123 and 124: This means for a classifier D: r i
Page 125 and 126: validation1, validation2 and test.
Page 127 and 128: 5.3.1 The Algorithm The steps of ou
Page 129 and 130: 5.4 Experimental Results In this ch
Page 131 and 132: sets. • We put as much as possibl
Page 133 and 134: argument is invalid in the context
Page 135 and 136: LDA and LDA produce the same accura
Page 137 and 138:
space. If the training data uniform
Page 139 and 140:
face for each subject, and compare
Page 141 and 142:
also be introduced. But class speci
Page 143 and 144:
These stages are discussed in the f
Page 145 and 146:
(a) (b) Figure A.2: Input fingerpri
Page 147 and 148:
(a) (b) Figure A.4: Orientation ima
Page 149 and 150:
(i) For each block centered at (i,
Page 151 and 152:
(a) (b) Figure A.7: Enhanced images
Page 153 and 154:
(a) (b) Figure A.10: (a) and (b) sh
Page 155 and 156:
(a) (b) Figure A.12: Binarized imag
Page 157 and 158:
(a) (b) Figure A.14: Thinned images
Page 159 and 160:
(a) (b) Figure A.16: Thinned finger
Page 161 and 162:
Paired Minutiae Minutiae with unmat
Page 163 and 164:
Bibliography [1] FVC 2002 website.
Page 165 and 166:
[25] Li-Fen Chen, Hong-Yuan Mark Li
Page 167 and 168:
[49] Lin Hong, Yifei Wan, and Anil
Page 169 and 170:
[71] L. Lam and C. Y. Suen. Applica
Page 171 and 172:
IEEE Tran. on Pattern Analysis and
Page 173 and 174:
of-the-Art Systems. IEEE Tran. on P
Page 175 and 176:
and Human Communication, pages 374-
show all

Master Thesis - Department of Computer Science

Create successful ePaper yourself

Delete template?

Save as template?