Master Thesis - Department of Computer Science

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the dimension of the search space is significantly decreased in the gradient method. Thomaz et al. [122] also studied on combining PCA and RBF neural network. Their system for face recognition consists of a PCA stage which inputs the projections of a face image over the principal components into a RBF network acting as a classifier. 2.1.1.4 Other Approaches • Range Data: One of the different methods used in face recognition task is using the range images. In this method data is obtained by scanning the individual with a laser scanner system. This system also has the depth information so the system processes 3-dimensional data to classify face images [23]. • Infrared Scanning: Another method used for face recognition is scanning the face image by an infrared light source. Yoshitomi et al. [137] used thermal sensors to detect temperature distribution of a face. In this method, the front- view face in input image is normalized in terms of location and size, followed by measuring the temperature distribution, the locally averaged temperature and the shape factors of face. The measured temperature distribution and the locally averaged temperature are separately used as input data to feed a neural network and supervised classification is used to identify the face. The disadvantage of visible ray image analysis is that the performance is strongly influenced by lighting condition including variation of shadow, reflection and darkness. These can be overcome by the method using infrared rays. • Profile Images: Liposcak and Loncaric [79] worked on profile images instead of frontal images. Their method is based on the representation of the original and morphological derived profile images. Their aim was to use the profile out- line that bounds the face and the hair. They take a gray-level profile image and threshold it to produce a binary image representing the face region. They nor- malize the area and orientation of this shape using dilation and erosion. Then, they simulate hair growth and haircut and produce two new profile silhouettes. From these three profile shapes they obtain the feature vectors. After nor- 22
malizing the vector components, they use the Euclidean distance measure for measuring the similarity of the feature vectors derived from different profiles. 2.1.2 Geometry Feature based Methods Geometry feature based methods uses the facial feature measures such as distance between eyes, ratio of distance between eyes and nose etc., but it is significantly different from the feature-based techniques that it constructs the topological graph using the facial features of each subject. • Graph Matching based Methods: In [69] Lades et al. presented a dynamic link architecture for distortion invariant object recognition which employs elas- tic graph matching to find the closed stored graph. Objects were represented with sparse graphs whose vertices were labeled with geometrical distances. In this system, individual faces were represented by a rectangular graph, each node labeled with a set of complex Gabor wavelet coefficients, called a jet. Only the magnitudes of the coefficients were used for matching and recognition. When recognizing a face of a new image, each graph in the model gallery was matched to the image separately and the best match indicated the recog- nized person. They presented good results with a database of 87 subjects and test images composed of different expressions and faces turned 15 degree. The matching process was computationally expensive, taking roughly 25 seconds to compare an image with 87 stored objects when using a parallel machine with 23 transputers. Wiskott et al. [131] extended this system to handle larger galleries and larger variations in pose and to increase the matching accuracy. Firstly, they use the phase of the complex Gabor wavelet coefficients to achieve an accurate location of the nodes and to disambiguate patterns which would be similar in the magnitudes of the coefficient. Secondly, they employ object adapted graphs, so that nodes refer to specific facial landmarks, called fiducial points. The correspondences between two faces can be found across large viewpoint changes. Thirdly, a new data structure called the bunch graph was introduced which serves as generalized representation of faces by combining jets of a small 23
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: faces of 40 subjects. • Others: A
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
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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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iii) Project all class means onto n
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Table 4.1: Effect of increasing num
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Table 4.3: Effect of increasing num
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(Original face) (Null face) (Range
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Table 4.6: Performance of Dual Spac
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5.1 Introduction In recent years, t
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This means for a classifier D: r i
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validation1, validation2 and test.
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5.3.1 The Algorithm The steps of ou
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5.4 Experimental Results In this ch
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sets. • We put as much as possibl
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argument is invalid in the context
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LDA and LDA produce the same accura
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space. If the training data uniform
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face for each subject, and compare
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also be introduced. But class speci
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These stages are discussed in the f
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(a) (b) Figure A.2: Input fingerpri
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(a) (b) Figure A.4: Orientation ima
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(i) For each block centered at (i,
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(a) (b) Figure A.7: Enhanced images
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(a) (b) Figure A.10: (a) and (b) sh
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(a) (b) Figure A.12: Binarized imag
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(a) (b) Figure A.14: Thinned images
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(a) (b) Figure A.16: Thinned finger
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Paired Minutiae Minutiae with unmat
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Bibliography [1] FVC 2002 website.
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[25] Li-Fen Chen, Hong-Yuan Mark Li
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[49] Lin Hong, Yifei Wan, and Anil
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[71] L. Lam and C. Y. Suen. Applica
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IEEE Tran. on Pattern Analysis and
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of-the-Art Systems. IEEE Tran. on P
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and Human Communication, pages 374-
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Master Thesis - Department of Computer Science

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