Master Thesis - Department of Computer Science

More documents

Recommendations

Info

[104] A. Rosenfeld and A. Kak. Digital Picture processing. Academic, New York, 1976. [105] A. Ross and R. Govindarajan. Feature Level Fusion Using Hand and Face Biometrics. In SPIE Conference on Biometric Technology for Human Identifi- cation, pages 196–204, Florida, U.S.A., March 2005. [106] A. Ross and A. K. Jain. Information Fusion in Biometrics. Pattern Recognition Letters, Special Issue on Multimodal Biometrics, 24(13):2115–2125, 2003. [107] Pankanti S., N.K. Ratha, and R.M. Bolle. Structure in Errors: A Case Study in Fingerprint Verification. In ICPR, pages III: 440–443, 2002. [108] C. Sanderson and K. K. Paliwal. Information Fusion and Person Verifica- tion using Speech and Face Information. In Research Paper IDIAP-PR 02-33, September 2002. [109] Bernhard Scholkopf, Alex J. Smola, and Andre Bernhardt. Nonlinear Com- ponent Analysis as a Kernel Eigenvalue Problem. Neural Computation, 10(5):1299–1319, 1998. [110] B. G. Sherlock, D. M. Monro, and K. Miller. Algorithm for Enhancing Finger- print Images. Electronics Letters, 28(18):1720, 1992. [111] B. G. Sherlock, D. M. Monro, and K. Miller. Fingerprint Enhancement by Directional Fourier Filtering. IEE Proceedings Vision Image and Signal Pro- cessing, 141(2):87–94, 1994. [112] T. Sim, S. Baker, and M. Bsat. The CMU Pose, Illumination, and Expres- sion Database. IEEE Tran. on Pattern Analysis and Machine Intelligence, 25(12):1615–1618, Dec. 2003. [113] Simon Haykin. Neural networks: A Comprehensive Foundation. Prentice-Hall International, New Jersey, 1999. [114] L. Sirovich and M. Kirby. Low-dimensional Procedure for the Characterization of Human Faces. Journal of Optical Society of America, 4(3):519–524, March 1987. [115] Robert Snelick, Umut Uludag, Alan Mink, Michael Indovina, and Anil Jain. Large-Scale Evaluation of Multimodal Biometric Authentication Using State- 152
of-the-Art Systems. IEEE Tran. on Pattern Analysis and Machine Intelligence, 20(3):450–455, March 2005. [116] J. Sprinzak and M. Werman. Affine Point Matching. Pattern Recognition Letters, 15:337–339, 1994. [117] V. S. Srinivasan and N. N. Murthy. Detection of Singular Points in Fingerprint Images. Pattern Recognition, 25(2):139–153, 1992. [118] R. M. Stock and C. W. Swonger. Development and Evaluation of a Reader of Fingerprint Minutiae. Technical Report CAL NO. XM-2478-X-1, Cornell Aeronautical Laboratory, 1969. [119] T. J. Stonham. Practical Face Recognition and Verification with WISARD. In Aspects of Face Processing, pages 426–441, 1984. [120] Daniel L. Swets and John (Juyang) Weng. Using Discriminant Eigenfeatures for Image Retrieval. IEEE Tran. on Pattern Analysis and Machine Intelligence, 18(8):831–836, Aug. 1996. [121] E. Szekély and V. Szekély. Image Recognition Problems of Fingerprint Identi- fication. Microprocessors and Microsystems, 17(4):215–218, 1993. [122] C. E. Thomaz, R. Q. Feitosa, and A. Veiga. Design of Radial Basis Function Network as Classifier in face Recognition using Eigenfaces. In Vth Brazilian Symposium on Neural Networks, pages 118–123, 1998. [123] M. A. Turk and A. Pentland. Face Recognition Using Eigenfaces. In Proc. 11th Int. Conf. Pattern Recognition, pages 586–591, 1991. [124] Matthew Turk and Alex Paul Pentland. Eigenfaces for Recognition. Journal of Cognitive Neuroscience, 3(1):71–86, 1991. [125] D. Valentin, H. Abdi, A. J. O’Toole, and G. W. Cottrell. Connectionist Models of Face Processing: A survey. Pattern Recognition, 27(9):1209–1230, 1994. [126] P. Verlinde and G. Cholet. Combining Decision Fusion Paradigms using k- NN based Classifiers, Decision Trees and Logistic Regression in a Multi-modal Identity Verification Application. In Proc. of Int. Conf. on Audio and Video based Biometric Person Authentication, pages 188–193, Washington D. C., U. S. A., March 1999. 153
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 and 86:
Table 3.5: Peak Recognition Accurac
Page 87 and 88:
Table 3.7: Performance of Ekenel’
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: IEEE Tran. on Pattern Analysis and
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?