Emerging Marketing Strategy of Yama Ribbons & Bows

厦门姚明织带饰品有限公司成立于2004年，公司专业生产高品质涤纶色丁丝带、涤纶罗纹丝带、涤纶织边印标丝带、尼龙雪纱带、丝绒带、丝带印刷、丝带小包装、发饰和花饰。自成立至今，短短几年时间，“姚明织带”专注于专业化和规模化生产，打造高品质产品，树立企业优异形象，企业信誉和产品质量在织带行业内公认最好，公司产品远销世界100多个国家和地区，年销售额超过4个亿，出口海外份额超过70%。 海外拓展始终是姚明织带营销的重要方向，然而近几年，随着欧美金融危机的恶化，海外市场销售额不断下降，如何开拓新兴市场潜能，实现持续增长，成为姚明织带迫在眉睫须处理的问题。 本文是篇案例型论文，通过笔者在姚明织带海外营...Xiamen Yama ribbon &bow Co., Ltd. was established in 2004, specialized in polyester satin ribbon, grosgrain ribbon, printing ribbon, cheer ribbon, velvet ribbon, retailer packaging, hair bow and accessories. In the Past 10 years, With good quality, nice service, huge stock and fast delivery, Yama Ribbon and Bows Co. is well recognized as one of the leading enterprises in ribbon industry in the wor...学位：工商管理硕士院系专业：管理学院_工商管理硕士(工商管理硕士)学号：1792011115071

NTCU誘発中期マウス肺扁平上皮がん新規モデルの開発

博士(医学)大阪市立大

中国の産業安全論争とその政策的反映(中)

開題―国家経済安全問題の歴史的位置と産業安全 Ⅰ産業安全に関する諸説 Ⅱ論争上の基本概念の検討 Ⅲ具体的事例との関連での議論 （第36巻第1号）Ⅳこの時点における国サイドの指導者の受け止め方・対応と外資側の反応 （以下本号） Ⅴ外資M&A 事例分析 Ⅵ外資管理体制整備への提言と取り組み （以上本号） Ⅶ国家対応政策措置 （以下次号） おわり

中国の産業安全論争とその政策的反映(下)

開題―国家経済安全問題の歴史的位置と産業安全 Ⅰ産業安全に関する諸説 Ⅱ論争上の基本概念の検討 Ⅲ具体的事例との関連での議論 （第36巻第1号）Ⅳこの時点における国サイドの指導者の受け止め方・対応と外資側の反応 （以下本号） Ⅴ外資M&A 事例分析 Ⅵ外資管理体制整備への提言と取り組み （以上第36巻第3号） Ⅶ国家対応政策措置 （以下本号） おわりに--議論の整

パネルディスカッション [日本語版]

金沢大学日中無形文化遺産研究所『国際シンポジウム「日中両国の方言の過去、現在、未来」報告書』, 金沢大学連携融合事業「日中両国における無形文化遺産保護と新文化伝統創出に関する共同事業」, 開催日 : 平成19年11月23日, 会場 : 金沢大学サテライトプラザ第三部　パネルディスカッション（全体讨论）, [汉语版]あ

パネルディスカッション [汉语版]

金沢大学日中無形文化遺産研究所『国際シンポジウム「日中両国の方言の過去、現在、未来」報告書』, 金沢大学連携融合事業「日中両国における無形文化遺産保護と新文化伝統創出に関する共同事業」, 開催日 : 平成19年11月23日, 会場 : 金沢大学サテライトプラザ第三部　パネルディスカッション（全体讨论）, [日本語版]あ

[[alternative]]An application of fuzzy logic and neural network to fingerprint recognition system

碩士[[abstract]]對自動指紋辨識系統而言，正確的特徵擷取是非常重要的。然而，品質不良影像中的雜訊常會造成特徵擷取錯誤，像是無法正確找出特徵點或是誤判特徵點。為了改善這些現象，目前有很多建立在精確數學模式之上的指紋辨識系統，嘗試解決此一問題，但是都無法適當地處理錯誤的現象。我們都知道，人們對於指紋圖案有極佳的辨識能力，因此，本篇論文運用類似人類思維的方式，應用模糊邏輯與類神經網路，成功地結合模糊理論具有容錯及倒傳遞類神經網路回想速度快之特性，實做具有容錯性且快速的指紋分析比對資料庫系統。每筆指紋資料經模糊化後，再輸入倒傳遞類神經網路訓練後建檔，所耗費的時間約為3秒。指紋資料庫比對每筆樣本平均需時0.08秒。系統在相似度閥值為0.9之情形下，其拒真率為0％，平均讓假率約為0.23％。由以上數據可得知此方法是強健、可靠且快速的。[[abstract]]The correct minutiae extraction is very important in an automatic fingerprint identification system. However, the presence of noise in poor-quality images will cause many extraction faults, such as the dropping of true minutiae and inclusion of false minutiae. Nowadays, most fingerprint identification systems are based on precise mathematical models, but they can not handle such faults properly. As we know, human beings are good at recognizing fingerprint pattern. Therefore, a human-like method is applied. This paper presents an adaptive fuzzy logic and neural network method which is fast and has variable fault tolerance. We implement a fast fingerprint database system with fault tolerance. Before neural network training, every fingerprint is encoded by a fuzzy image encoder. Then the result of training is saved in a database. The training time is 3 seconds. The matching time is 0.08 second. When the threshold is 0.9, the FAR is 0% and FRR is 0.23%. Our experimental results have shown that this fingerprint identification method is robust, reliable and rapid.[[tableofcontents]]目 錄 第一章 緒論……………………………………………1 1.1 研究動機………………………………………1 1.2 自動指紋辨識系統……………………………2 1.3 相關研究………………………………………3 1.4 論文架構………………………………………5 第二章 指紋影像前處理與分叉點特徵萃取 ………6 2.1 指紋影像背景知識……………………………7 2.2 指紋影像前處理與分叉點特徵萃取 …… 10 2.2.1 正規化 ………………………………12 2.2.2 Gabor Filter …………………………13 2.2.3 二值化 ………………………………15 2.2.4 細線化 ………………………………17 2.2.5 分叉點的萃取 …………………………21 2.2.6 後處理 ………………………………23 第三章 模糊影像編碼器 ……………………………25 3.1 簡介 …………………………………………26 3.2 模糊影像編碼器實作……………………… 29 第四章 倒傳遞類神經網路 …………………………34 4.1 簡介 …………………………………………35 4.2 類神經網路的運作過程…………………… 38 4.3 倒傳遞類神經網路的演算法……………… 39 4.4 本研究的網路架構………………………… 43 第五章 自動指紋辨識系統………………………… 46 5.1 實驗樣本 ……………………………………47 5.2 系統流程…………………………………… 49 5.2.1 指紋資料庫訓練流程（模式一） ……49 5.2.2 指紋資料庫訓練流程（模式二） ……50 5.2.3 指紋辨認測試流程 ……………………53 5.3 結果與討論 …………………………………54 5.3.1指紋影像的旋轉容錯性……………… 54 5.3.2 指紋影像的位移容錯性 ………………60 5.3.3 隨機減少特徵點 ………………………64 5.3.4 模糊影像大小與性能的關係 …………65 5.3.5 每一指紋影像的處理時間 ……………66 5.3.6 匹配速度 ………………………………67 5.3.7 模式二訓練下之可變式的容錯性 ……68 5.3.8 拒真率 …………………………………71 5.3.9 認假率 …………………………………72 第六章 結論 …………………………………………73 參考資料………………………………………………74 圖 目 錄 圖 2.1 指紋端點與分叉點示意圖 ………………………………… 7 圖 2.2 指紋細微特徵（點、島、突刺、橋點、短山脊、分叉點）… 9 圖 2.3 指紋細微特徵（端點、交叉點）…………………………… 9 圖 2.4 前處理與分叉點特徵萃取流程圖………………………… 11 圖 2.5 經過Gabor Filter後的指紋影像 ………………………14 圖 2.6 二值化後的指紋影像 …………………………………… 16 圖 2.7 細線化後的指紋影像 …… …………………………………17 圖 2.8 細線化演算法採用之視窗 …………………………………18 圖 2.9 舉例說明 ……………………………………………19 圖 2.10 利用 函數分辨端點與分叉點………………………… 22 圖 2.11 指紋影像處理後得到的結果…………………………………24 圖 3.1 吊鐘型歸屬函數………………………………………………28 圖 3.2 傳統一維模糊集合……………………………………………28 圖 3.3 影像分成64（8x8）個格子………………………………… 31 圖 3.4 二維模糊化歸屬函數 ……………………………………… 32 圖 3.5 歸屬函數參數示意圖……………………………………… 32 圖 3.6 指紋分叉點的模糊影像…………………………………… 33 圖 4.1 雙彎曲函數 ………………………………………………… 37 圖 4.2三層倒傳遞類神經網路架構圖……………………………40 圖 4.3 倒傳遞類神經網路架構………………………………………45 圖 5.1 自動指紋辨識系統操作介面 ……………………………… 48 圖 5.2 指紋資料庫訓練流程（模式一）……………………………51 圖 5.3指紋資料庫訓練流程（模式二）……………………………52 圖 5.4 指紋辨認測試流程……………………………………………53 圖 5.5 萃取出的分叉點與原始指紋影像的對照圖 ……………… 55 圖 5.6 原始影像經模糊化後的特徵影像……………………………55 圖 5.7 將萃取出之分叉點指紋影像順時針旋轉5∘… … …… 57 圖 5.8 將原始影像經模糊化後的特徵影像，順時針旋轉5∘後所得 到的模糊影像 ………………………………………… 57 圖 5.9 指紋旋轉對系統的影響………………………………………59 圖 5.10 由水平方向位移指紋對系統的影響…………………… 61 圖 5.11由垂直方向位移指紋對系統的影響 ………………………61 圖 5.12 由任意方向位移指紋對系統的影響 ……………………62 圖 5.13 隨機減少分叉點的辨識結果………………………………64 圖 5.14模式二訓練下之可變式的容錯性 …………………………69 表 目 錄 表 5.1 特徵影像所對應的數值…………………… 56 表5.2特徵影像順時鐘旋轉5∘後所對應的數值 … …………………………………………… 58 表 5.3 同時旋轉與任意方向位移的容錯範圍…… 63 表 5.4 模糊影像大小與性能的關係……………… 65 表 5.5 模式二訓練下可變式之容錯性的時間成本與 FAR值……………………………………… 70[[note]]學號: 792350075, 學年度: 9

[[alternative]]生物識別系統之分析與研究

博士[[abstract]]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。[[abstract]]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.[[tableofcontents]]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[[note]]學號: 894350106, 學年度: 10