現今社會上存在著失蹤人口的問題，並缺乏適當的管道來協助尋找這些失蹤的人，特別是失智老人，他們因記憶力退化無能力照顧自己，一旦走失流落街頭將無法找到回家的路，其處境不僅令人憂心，返家之路亦成為遙不可及的事。當面臨親人走失時，家屬也只能透過警政單位、媒體、張貼失蹤者的照片等等方式來尋找親人，此過程有如大海撈針般的辛苦以及遍尋不著的無助。對於這些協尋的失蹤者，往往需歷經數年，他/她們的面容會有改變，如果能年輕化他們的臉孔將有助於讓其認識他/她們的人來辨認，進而提供警方/家屬的協尋，以便讓其失智的老人能夠順利的返家。因此，若能發展人臉模型之自動老化/年輕化的合成系統，不僅對保護失智老人是一重要的課題，並且對於家屬尋找家中失智的老人，亦會是一個很大的幫助。
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。
此外，隨著電子銀行、電子商務、智慧卡、3C產品與雲端科技時代的來臨，對於極端重視隱私及保護個人資訊安全的現今社會，自動個人識別已成為一個非常重要的話題。對於安全防護較低的密碼識別，正被逐漸的淘汰中。因此，運用人體與生俱來且具有獨一無二之特性，即身份密碼來做辨識，已掀起一股風潮，目前正被世界各國及各類產品廣泛的應用。
由於「指紋」具有唯一性、可攜性、不易偽造、亦不會遺忘與借出等等特性，所以，目前運用生物特徵來做為辨識的方法之中，以「指紋辨識」為當今之首要之選。
雖然指紋辨識技術已經在過去的40年間迅速發展，但仍具有一些挑戰性的研究課題。重疊指紋的處理與匹配就是一個具有挑戰性且較少受到注意的問題。由於，現有的指紋特徵擷取演算法的運用是假設指紋影像只有一枚指紋，所以，不能正確地處理重疊的指紋。因此，如何有效的將重疊的指紋分離，是一個相當重要且必要的步驟。
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
References	115

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 [43], 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
calibration                                                                                                    31
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
                                                                                                                   41
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
                                                                                                                   45
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
estimation                                                                                                 103
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 [5]                           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[94]                                                                          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
                                                                                                                                    110

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