以視訊為基礎之手寫簽名認證

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姓名

羅尉賢(Wei-Hsien Lo) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

以視訊為基礎之手寫簽名認證
(Video-based Handwritten Signature Verification)

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摘要(中)

本篇論文提出以視訊為基礎進行手寫簽名認證，取代傳統使用的數位手寫板。原因為網路攝影機此項硬體設備比數位手寫板來得普及，較易取得也較便宜，可以降低成本的需求，並且在特徵資訊擷取上，能獲得的資訊也比數位手寫板來的多。傳統使用數位手寫板能擷取的特徵資訊主要集中在文字本身，但若使用網路攝影機，能擷取的資訊除了文字，還包含簽名者握筆姿勢的影像資訊。因此本篇論文提出兩種特徵資訊來進行簽名認證，一是以簽名文字為特徵的靜態資訊，使用曲波變換(curvelet transform)製作成特徵向量，另一是以簽名者握筆姿勢為特徵的動態資訊，使用motion energy image (MEI)製作成特徵向量，將使用上述兩種特徵資訊之認證流程串聯來進行手寫簽名認證，可得良好的結果錯誤接受率0％和錯誤拒絕率0.5％，在模仿簽名的部份錯誤接受率0.05％亦是如此。

摘要(英)

This paper proposes a video-based handwritten signature verification framework. When acquiring signature information, we use a webcam in substitution for a digitizing tablet. Because webcams are more prevalent and cheaper than digitizing tablets, using webcams as sensors can reduce the cost. In addition, the features extracted using a webcam also contain more information. In tradition handwritten signature verification, features extracted using a digitizing tablet are mainly trajectories. But for the features extracted using a webcam, we can acquire pen grasping posture information of the subscriber in addition to the trajectories of the signature. Therefore, in the proposed framework, we perform video-based handwritten signature verification using two different types of feature information. For the first type of feature, we perform curvelet transform on the subscriber’s writing trajectory to obtain static information. The second type of feature is dynamic information which is the pen grasping posture of the subscriber. The dynamic feature is represented by motion energy image (MEI). We cascade the classifiers using static information and dynamic information to perform handwritten signature verification. The proposed video-based handwritten signature verification framework achieves a low false acceptance rate of 0% and false rejection rate 0.5% for our handwritten signature database without imitation signatures. For the database with imitation signatures, the proposed framework can also achieve a low false acceptance rate of 0.05%.

關鍵字(中)

★ 簽名認證
★ 曲波變換
★ 移動能量圖

關鍵字(英)

★ curvelet transform
★ motion energy image
★ signature verification

論文目次

摘要..............................................i
Abstract.........................................ii
目錄............................................iii
附圖目錄..........................................v
附表目錄.........................................vi
第一章緒論.......................................1
1-1研究目的..............................1
1-2文獻探討..............................1
1-3系統架構..............................4
第二章相關技術...................................6
2-1 k-means..............................6
2-2粒子濾波器（particle filter）.........7
2-3曲波變換（curvelet transform）........8
2-4 Motion Energy Image（MEI）..........11
2-5 Principal Component Analysis（PCA）.12
2-6 Linear Discriminant Analysis（LDA）.13
第三章系統架構..................................16
3-1筆尖偵測與追蹤.......................16
3-2靜態資訊.............................20
3-3動態資訊.............................21
3-4訓練以及認證方法.....................24
第四章實驗結果與討論............................26
4-1環境設定.............................27
4-2資料庫介紹...........................27
4-3特徵資訊的擷取.......................29
4-3.1 靜態資訊..........................29
4-3.2 動態資訊..........................29
4-4實驗結果.............................31
4-4.1 閥值的選擇........................31
4-4.2 真實簽名資料庫實驗結果............34
4-4.3 模仿簽名資料庫實驗結果............34
4-4.4 各種特徵資訊比較..................34
4-4.5 是否使用zero mean之比較...........37
第五章結論與未來工作............................38
第六章參考文獻..................................42

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指導教授

鄭旭詠(Hsu-Yung Cheng)

審核日期

2010-7-9

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