臺灣博碩士論文加值系統

隨著科技的發展，許多機密性文件的保存與使用者身份的管理變得格外的重要。舉凡門禁管制系統、金融管理、犯罪偵查與電腦認證等等，都是需要借助一套強而有力的身份確認系統。所以身份確認技術在二十一世紀的資訊化社會中將扮演愈來愈重要的角色，如何建構一套既安全又便捷的身份確認系統是目前學界與業界都很熱衷的研究課題。
由於目前大多數的身份確認研究都單只針對個人生理特徵中的某一項著手，這比起使用多個生理特徵來做辨識，其辨識率顯然較低。所以本研究以能提高系統辨識率為目的，結合人臉和掌形兩項生物特徵為出發點，發展出一套多項生物特徵的身份確認系統。本系統利用臨界值法、邊緣偵測、影像外形處理與影像投影等基本影像處理自動找出特徵點的座標後，計算出其所對應的特徵向量組合。在比對方面，由於特徵向量代表每個身份的特徵值，因此本研究利用歐幾里德距離和漢明距離之差異值計算法來比對特徵向量間的相似程度，以達成辨識的目標。在完成理論的推演後，本研究除產生一完整的演算法則外，最後並以實際人臉及手掌影像來驗證本研究所提之多項生理特徵之身份確認系統的實際功效。

Along the modern technology development, the preservation of many confidential documents and the management of user identity have become more important. It may include the entrance control system, financial management, criminal detection, and computer certification etc., which all require a set of strong identity verification system. Therefore, identity verification technique will play a more important role in the informationalized society of the 21st century, and how to construct a set of safe and convenient identity verification system has been the hot study topic in the academic and industrial field.
Most current identity verification study has been aimed at certain part of the individual physical feature, which made the identification rate much lower comparing to the use of multiple physiological features for identification. Therefore, the main purpose of this study is to upgrade the identification rate by combining both the human face and the hand geometric features, and to develop a set of identity verification system with multiple biological features. In this system, the basic image processing techniques including thresholding method, edge detection, image form treatment and image projection etc, are used to find out the coordinates of feature points automatically, and then to calculate their combination of corresponding eigenvectors. On the comparison aspect, due to the eigenvector includes the eigenvalues of each identity, therefore, this study has made the use of difference value calculation methods including Euclidean distance and Hamming distance to compare and to check the degree of similarity among the eigenvector to achieve the goal of identification. Upon completion of the theoretical inference, this study, in addition to produce a complete calculation method, has also proved the practical effect of the identity verification system with multiple physical features by means of both human face and palm shape images.

中文摘要 ---i
英文摘要 ---iii
誌謝 ---v
目錄 ---vi
表目錄 ---ix
圖目錄 ---xiii
第一章 緒論---1
1.1 簡介---1
1.2 研究動機與系統架構---2
1.3 文獻回顧---5
1.4 論文架構---9
第二章 基本影像處理---10
2.1 臨界值法---11
2.2 邊緣偵測---13
2.3 影像外形處理---16
2.3.1 開閉運算---16
2.3.2 細線化---20
2.3.3 連通成分的標示---23
2.3.4 區域填充---27
2.4 影像投影---29
第三章 人臉特徵擷取---31
3.1 臉部偵測---33
3.1.1 HSV色彩轉換---33
3.1.2 膚色分割---35
3.2 相對位置之特徵擷取---39
3.2.1 適應性臨界值---40
3.2.2 嘴巴影像處理---41
3.2.3 鼻子影像處理---42
3.2.4 眼睛影像處理---43
3.3 特徵擷取---44
3.3.1 特徵點---45
3.3.2 特徵向量---47
第四章 掌形特徵擷取---50
4.1 掌形偵測---52
4.1.1 取像設備---52
4.1.2 掌形輪廓影像處理---55
4.2 掌形特徵擷取---59
4.2.1 曲線追蹤---60
4.2.2 特徵點---62
4.2.3 掌形特徵---63
第五章 實驗結果---69
5.1 實驗安排---69
5.2 比對方法---77
5.2.1 歐幾里德距離---77
5.2.2 漢明距離---79
5.3 辨識結果---81
第六章 結論---123
參考文獻 ---124
附錄一 使用歐幾里德距離與漢明距離資料庫建檔1組比對數據---128
個人資料 ---148

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