臺灣博碩士論文加值系統

由於指紋的獨特性可被使用於識別身分，訪問管制和刑事調查是主要使用的兩個領域。在指紋分析的過程中，透過特徵點位置進行指紋比對以確認是否為同類指紋，因此特徵點的定位準確度十分重要。在Henry及FBI對特徵點位置的定義難以透過計算機程序完成，必須依靠人類專家進行定位，且定位方法較為主觀，而Liu使用深度神經網絡提出了一種計算式定義特徵點的方法，可在像素級定義特徵點並能在非常小的圓中有效地定義特徵點。
在本論文中，將依照 Liu在2018年所發表的”Fingerprint Analysis and Singular Point Definition by Deep Neural Network”中的方法進行驗證所提出計算式定義核心點位置的演算法正確性，是否能夠定位於實際核心點位置。
根據結果顯示，此演算法在箕形紋類型的指紋上容易出現定位偏移的問題，此外，在此論文中也提出了校正偏移定位的想法。

The unique nature of fingerprints can be used to identify identity. Access control and criminal investigation are two major fields that take advantages of the unique nature of fingerprints. In the process of fingerprint analysis, fingerprint comparison through the position of singular points to confirm whether there are the same fingerprint. Therefore, the positioning accuracy of singular point is very important. The definition of the position of singular points in Henry and FBI are very hard to implement by computer programs. They must rely on human experts to locate, and the positioning method is more subjective than computational definitions. Liu uses a deep neural network to propose a the method of computational definition of singular point. The proposed method defines singular point at the pixel level and efficiently define singular point. in a very small circle
In this paper, the experiment verify correctness of the algorithm for defining the position of the singular point according to the method of "Fingerprint Analysis and Singular Point Definition by Deep Neural Network" published by Liu in 2018 , and determine if it can be located at the actual location of singular point .
The results suggested that the algorithm is prone to the problem of positioning offset on the fingerprint of loop type. In addition, the idea of correcting offset positioning has also been proposed in this paper.

摘要
Abstract
致謝詞
目錄
圖目錄
表目錄

第一章 緒論
1.1 研究背景
1.2 研究目的
1.3 研究問題

第二章 文獻探討

第三章 指紋分析介紹
3.1 指紋類型介紹
3.2 指紋分析流程

第四章 實驗設計
4.1 載入模型
4.1.1 模型訓練資料
4.1.2 深度學習模型-卷積深度神經網路
4.1.2.1 卷積深度神經網路簡介
4.1.2.2 使用模型之深度神經網路架構
4.1.3 應用模型實驗流程
4.1.3.1 載入影像
4.1.3.2 使用模型於載入影像
4.2 定位指紋特徵點
4.2.1 N方向定位圖
4.2.1.1 轉換N方向圖像的定義
4.2.1.2 三方向三色圖及四方向四色圖的角度色彩配置
4.2.1.3 原始影像上繪製N方向N色圖的交界邊線
4.2.2 特徵點定位
4.2.3 像素化特徵點定位圖
4.2.3.1 像素等級的三方向三色圖
4.2.3.2 圖像平滑化(smooth)
4.2.3.3 原始影像上繪製像素化三色圖交界邊線

第五章 研究結論
5.1 討論
5.1.1 核心點位置偏移分析
5.1.1.1 核心點偏移原因
5.1.1.2 預測核心點可信度
5.1.2 核心點偏移校正
5.2 研究結論

參考文獻

附錄
附錄A : 常用指紋鑑識名詞對照表
附錄B : 特徵點定位準確範列
附錄C : 特徵點定位偏移範列

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