||Analysis and Research on Biometric Identification System
||Department of Electrical Engineering
目前在人臉之不同年齡的合成系統中，都沒有強調五官對齊及扭曲影像的校正，若有這兩種情形，可能會導致影像上的失敗與合成上的不準確，在本研究中，我們提出一個整合ASM演算法與Log-Gabor wavelet的方法來達到人臉影像之老化/年輕化合成可逆系統，以便應用於失智老人之協尋。首先，我們利用ASM演算法可得到一組描述人臉五官特徵及輪廓的特徵集，將此組特徵集透過本系統的內眼角不變性及幾何不變性來達到人臉影像的校正。並再利用各特徵值間的相似程度，來判別臉型,以利搜尋與測試臉孔相似之樣本影像。接著，我們利用Log-Gabor wavelet轉換解析人臉影像之年齡紋理，以得到分解圖像，再過分解圖像數量的控制，有效地模擬出不同年齡之人臉合成，最後利用皺紋密度的方法來客觀判定合成的結果。
因此，本研究提出基於方向場之重疊指紋分離演算法。在方向場的估算部份，採用local Fourier analysis來決定方向場的方向，透過方向場的提供，再使用Gabor filter來取出正確的指紋。然而，錯誤的方向場會導致錯誤之指紋分離的結果，所以，在克服雜訊干擾的部分，利用機率密度函數的概念以及多尺度之技巧，來修正錯誤的方向場。並藉由相關性的量測，透過數學公式的計算，可以有效的鑑別分離之指紋的正確性。
||The issues with missing persons in the present society and the lack of suitable channels to assist in locating those lost in the streets, particularly seniors with dementia whose memory degradations result in their inabilities to find the way home, are worrying as going home on their own is almost an impossible task. With their loved ones lost in the streets, the families could only search for the missing persons via the police, media and posting of photographs, during which all involved have to endure the anxieties, frustrations and helplessness of the process similar to finding a needle in a haystack. For the missing persons, their facial appearances may change in the years spent lost in the streets, hence by making their faces younger to facilitate better recognitions by those familiar with the missing persons and aid in the searches by the police or families, the opportunities for the seniors with dementia to return to their homes may be increased. Therefore, the development of the synthetic system for automatic aging/ reverse aging of facial models is not only an essential topic for the protection of seniors with dementia but also a significant contribution to the search efforts of the families.
The existing synthetic systems for the faces at various ages do not emphasize on the alignment of facial characteristics and the calibration of distorted images, which are conditions that may lead to failed attempts or inaccuracies in the synthesized images. In this study, a method integrating ASM algorithm and Log-Gabor wavelet is proposed to achieve a reversible synthetic system for the aging/reverse-aging of facial images, which may be applied to the searches for seniors with dementia. First, facial detection of the ASM algorithm are used to collect a set of features describing the characteristics and contours of the faces, which is then calibrated by the system via the invariance and geometric invariance of the inner corner of the eyes. The levels of similarity between the feature values are utilized to determine the face types for searching and testing with similar sample images. Then the Log-Gabor wavelet transformation is implemented to analyze the aging textures of the facial images to obtain the decomposed images, so that the synthetic faces of various ages may be effectively simulated by controlling the number of decomposed images and finally the wrinkle intensity method is applied to objectively determine the results of the synthesis.
Furthermore, in the dawning era of e-banking, e-commerce, smartcards, 3C products and cloud technologies, automatic personal identification has become an extremely important topic as the modern society places ever-increasing emphasis on the privacy and secure protection of personal information. Password identification is being phased out gradually due to its low levels of security. Therefore, the preference for the use of characteristics naturally inherent and unique to each human being as personal passwords for identification is now being widely applied in numerous types of products in countries around the world.
The “fingerprint” is unique, portable, difficult to forge, could not be forgotten and loaned, hence these properties render “fingerprint identification” as the top choice amongst the biometric identification methods at present.
Although the fingerprint identification technology has developed rapidly over the past 40 years, some challenging research topics remain to be resolved. The processing and matching of overlapping fingerprints, created when one or more fingers with multiple contacts on the same location of an object, is a challenging issue lacking attention. However, as the existing minutiae extraction algorithms assume only one fingerprint per image, the overlapping fingerprint data could not be properly processed. Therefore, the effective separation of overlapping fingerprints is an extremely important and essential process.
Hence, this study proposes an algorithm for the separation of overlapping fingerprints based on the orientation fields. The local Fourier analysis is utilized for the initial orientation field estimation and the Gabor filter is subsequently used with the orientation field to extract the fingerprint information corresponding to the orientations. However, the wrong orientation fields may lead to erroneous results in the separation of fingerprints, thus to overcome the noise interferences, the concepts of probability density function and multi-scale technique are implemented for corrections. And the accuracy of the separated fingerprints may be evaluated effectively by using the correlation measurements with mathematical calculations.
||Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Research Objective 3
1.3 Background and Related Work 5
1.4 Organization of Dissertation 9
Chapter 2 Literature Reviews 10
2.1 Fingerprint Image Processing 10
2.2 Facial Image Algorithm 22
2.2.1 ASM algorithm 22
2.2.2 Log-Gabor wavelet 23
Chapter 3 Synthesis System of Facial Image 26
3.1 System Structure 26
3.2 Calibration of the Faces 27
3.3 Facial Feature Analysis 37
3.4 Aging Synthesis 40
3.5 Reverse-Aging Synthesis 43
3.6 Experimental Results 47
3.6.1 Facial image database 47
3.6.2 Comparison of the calibration methods for the facial characteristics 49
3.7 Determination of Age 54
3.7.1 Contrast enhancement 54
3.7.2 Determination of age 55
3.8 Conclusions 65
Chapter 4 Fingerprint Recognition by MOPSO Hybrid with SVM 66
4.1 Introduction 66
4.2 Optimization Algorithm and Support Vector Machine 68
4.2.1 Multi-objective optimization 68
4.2.2 Support vector machine 73
4.2.3 Multi-objective SVM 75
4.3 Fuzzy Encoder on Fingerprint 81
4.3.1 Feature extraction 81
4.3.2 Fuzzy image encoder 83
4.4 Experimental Results 85
4.5 Conclusions 86
Chapter 5 Separation of Overlapping Fingerprints 87
5.1 Related Work 87
5.1.1 Fourier Transform(FT) 87
5.1.2 Binarization 88
5.1.3 Gabor filter 88
5.1.4 Local Fourier descriptor 89
5.2 Proposed Method 90
5.2.1 Algorithm flow 94
5.3 Experimental Results 98
5.3.1 Experimental parameters/Environment settings 98
5.3.2 Results of the fingerprint separation 99
5.4 Assessment of the Accuracy after the Separation of
Overlapping Fingerprints 105
5.5 Conclusions 112
Chapter 6 Summary and Future Work 113
LIST OF FIGURES
Figure 1 1 Skull model 8
Figure 1 2 Age space for aging and rejuvenating 8
Figure 1 3 Age synthesis using average values 8
Figure 1 4 Age synthesis using principal component analysis (PCA) 8
Figure 2 1 FVC2006 DB3: A 16x16 pixels detail (b) from the original image (a) with
the window marked as a white square 11
Figure 2 2 The fingerprint image fade into a corresponding OF image calculated from
a 16x16 square-mesh 13
Figure 2 3 (a) fingerprint image of low qualities; (b) the OF image of the fingerprint
in (a) is calculated with the method in , the OF of some elements are
not consistent thus normalization is necessary, (c) the average local results
of the normalized OF image for each element in (b), which is obtained
from the 3x3 window according to the formula (2) 16
Figure 3 1 System Structure 27
Figure 3 2 Flowchart of the preprocessing 29
Figure 3 3 The distribution of ASM feature points (red dots indicate the feature points
frequently used in this study) 30
Figure 3 4 Rotational calibration of the image (a) Before calibration (b) After
Figure 3 5 Cropping for the face (a) Original image (b) Cropped facial image 32
Figure 3 6 The results of scaling based on the width and distance of the facial
contours and the invariance of the inner corner of the eyes 33
Figure 3 7 The invariance of the inner corner of the eyes in the scaling of images 34
Figure 3 8 The result of the image height calibration 35
Figure 3 9 Flowchart of the height calibration 36
Figure 3 10 Flowchart of trimming and alignment of the image 37
Figure 3 11 Decomposition Map 39
Figure 3 12 Decomposition Map 40
Figure 3 13 High frequency components of the decomposition maps at the target age
Figure 3 14 Flowchart of the facial synthesis 42
Figure 3 15 Age synthesis by adding high frequency data 43
Figure 3 16 Decomposition Map 44
Figure 3 17 High frequency components of the decomposition maps at the target age
Figure 3 18 Reverse age synthesis process 46
Figure 3 19 Image of reverse-aging synthesis of the faces 46
Figure 3 20 Customized facial image database(female) 48
Figure 3 21 Customized facial image database (male) 48
Figure 3 22 Reference image 49
Figure 3 23 Comparison of the calibration systems for the eyes 50
Figure 3 24 Comparison of the calibration systems for the nose 50
Figure 3 25 Comparison of the calibration systems for the mouth 50
Figure 3 26 Comparison of the calibration systems for all 50
Figure 3 27 Comparison of the calibration systems for the facial characteristics 51
Figure 3 28 Synthesis of aging faces (a) Before aging synthesis (original image) (b)
After aging synthesis 52
Figure 3 29 Example graphs of contrast enhancement (a) before contrast
enhancement (b) after contrast enhancement 55
Figure 4 1 Dominance relationships- two minimization objective functions 69
Figure 4 2 Structure of MOPSO – a sample pseudo-code 70
Figure 4 3 Pareto frontier - Deb1 72
Figure 4 4 Pareto frontier - Deb2 73
Figure 4 5 Pareto frontier - ZDT-1 73Figure 4 6 Samples tested to determine if the classifier is feasible ( class 1: red point,
class 2: blue points, classifier: black line) 78
Figure 4 7 Results of the Pareto frontier for the samples 79
Figure 4 8 Maximum margin for the Pareto solutions 79
Figure 4 9 Prediction error (in our research) 79
Figure 4 10 Figure 4 10 Prediction error and Training error comparison: the y-axis
denotes the prediction error for the training (+) and testing (*) data and
the x-axis denotes a counter over all of the Pareto-optimal solutions
ordered by training errors Note that all of the error is generalized 80
Figure 4 11 Original fingerprint image (NIST-4) 83
Figure 4 12 Minutiae extraction – bifurcation 83
Figure 4 13 The fuzzy image of fingerprint bifurcation structure 84
Figure 4 14 Pareto frontier for the fingerprint images in the dataset 85
Figure 5 1 An image of three overlapping fingerprints 90
Figure 5 2 The original fingerprint image is shown in the (a), and the image extracted
from the erroneous orientation field is shown in the (b) 92
Figure 5 3 The OF values corresponding to the top 20 amplitudes after the FFT
process are shown in the (a), the results in the (b) is divided into five
quantification intervals for statistical analysis Based on the experimental
results, the 20 sample points are divided into 10 intervals in this study 93
Figure 5 4 The original image is shown in the (a), the image without enhancement is
shown in the (b) 94
Figure 5 5 Flowchart for the separation of overlapping fingerprints 97
Figure 5 6 The overlapping fingerprint images in the database 98
Figure 5 7 Separation tests of the fingerprints while temporarily ignoring the
boundaries The original test image is shown in the (a) And the results of
the separation are shown in the (b) and (c) 100
Figure 5 8 The results of the separation of overlapping fingerprints The original test
image is shown in the (a) And the results of the separation are shown in
the (b) and (c) 101
Figure 5 9 The results of the separation of overlapping fingerprints The original test
image is shown in the (a) And the results of the separation are shown in
the (b) and (c) 102
Figure 5 10 Illustrates the overlapping fingerprint images of the two fingerprints, number
101_3 +102 _4, in the database 103
Figure 5 11 The separation of fingerprint images obtained from the erroneous OF
Figure 5 12 Illustrates the fingerprint image separated by using the method proposed
in this study 103
Figure 5 13 Illustrates the overlapping fingerprint images of the two fingerprints,
number 101_3 +107 _4, in the database 104
Figure 5 14 The separation of fingerprint images obtained from the OF estimation
based on this research 104
Figure 5 15 Corr = 0 600 This result exhibits the lowest correlation value in the
selection of the correct separation of fingerprints It is observed that
erroneous feature points are derived from the separation in this study at
the coordinates of (x, y) = (15, 40), but correct feature points are still
obtained from the separation in this study at (x, y) = (33, 20) Overall,
this separation image is functional with an accurate orientation field 106
Figure 5 16 Corr = 0 676 Completely accurate fingerprint lines obtained from the
separation in this study 106
Figure 5 17 Corr = 0 789 It is observed that the correct feature points are derived
from the separation in this study at (x, y) = (30, 40) 107
Figure 5 18 Corr = 0 796 The correct feature points are derived from the separation in
this study at (x, y) = (40, 40) 107
Figure 5 19 Corr = 0 148 108
Figure 5 20 Corr = 0 311 108
Figure 5 21 Illustrates the overlapping fingerprint images of the two fingerprints,
number 101_3 +102 _4 110Figure 5 22 Illustrates the overlapping fingerprint images of the two fingerprints,
number 101_3 +107 _4 111
Figure 5 23 Illustrates the overlapping fingerprint images of the two fingerprints,
number 108_3 +101 _4 111
LIST OF TABLES
Table 1 1 Comparisons of Biometrics Identification Technologies  2
Table 3 1 Image Adjustment 38
Table 3 2 Statistics of the facial image database 47
Table 3 3 Statistical results of the accumulated images with the ASM algorithm 51
Table 3 4 Comparison of the experimental results of method 1 52
Table 3 5 Comparison of the experimental results of method 2 53
Table 3 6 Statistical data of method 1 and 2 53
Table 3 7 Categories of age 56
Table 3 8 Examples of wrinkle density 57
Table 3 9 Examples of wrinkle density 58
Table 3 10 Examples of wrinkle density 59
Table 3 11 Examples of wrinkle density 60
Table 3 12 Examples of wrinkle density 61
Table 3 13 Examples of wrinkle density 62
Table 3 14 Average value and standard deviation of the wrinkle density 63
Table 3 15 Results of the determination of age for the aging synthesis 64
Table 3 16 Results of the determination of age for the reverse-aging synthesis 65
Table 4 1 Test function for MOO 71
Table 4 2 Parameters for the test function in Table 4 1 72
Table 4 3 MOPSO-SVM parameter for classifying fingerprint in NIST-4 85
Table 4 4 Experimental results 86
Table 5 1 Calculation of the rate of accuracy by using complete fingerprints as units
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