利用虛筆資訊特徵作中文簽名確認

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姓名

賴承澤(Shen-Zhe Lai ) 查詢紙本館藏

畢業系所

資訊工程研究所

論文名稱

利用虛筆資訊特徵作中文簽名確認
(Chinese Signature Verification Utilizing Virtual-Stroke Information)

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摘要(中)

隨著電子化時代的來臨，人類的各種行為不可避免的將與資訊科技相結合。在此潮流之下，傳統的保密與認證方式如識別碼，已難以應付日益龐雜的電子交易與資訊保密措施等的需求。基於生物特徵難以複製或遭竊的特性，發展以個體在生物特徵上的辨識方法，將是補強這個缺口的最佳方法之一。
可供做個人身分確認的生物特徵有許多種，大致可分為兩類。一是行為上的特徵，例如：簽名、聲音，另一種則是生理學上的特徵，例如：掌紋、指紋、掌型、臉型、虹膜…等。隨著信用卡消費方式的普及，以個人簽名作為身分確認的方式亦廣為被大眾接受。本論文將以簽名辨識為對象，尤其將針對簽名時筆劃與筆劃間提筆的軌跡（虛筆）。以簽名的筆劃外型（實筆）作為辨識對象時，難以分辨出模仿技巧高超的仿冒簽名，但虛筆的軌跡對人眼來說幾乎是不可見的，更遑論模仿，卻可利用電子設備將虛筆軌跡紀錄下來。並以簽名的外形作為主要的辨識依據，將一維的簽名點資料轉換成二維的簽名影像，利用以離線簽名確認為主的技術做處理以及辨識。以此發展識別系統，將可杜絕相當程度的仿冒簽名。

摘要(英)

The information technologies are now more and more blended into our daily life as the coming of electronic era. Traditional security ways, such as PIN code, are no longer reliable and difficult to satisfy the complex behaviors of e-commerce and information security. Unlike PIN code or passwords, biometrics can not be easily duplicated or stolen. Hence, they are more trustworthy and secure for identity verification.
There are various kinds of biometrics that can be utilized for personal identification. With the popularity of credit cards, signature plays a very important role in authenticity and authorization. People are getting more familiar with the process of handwritten signature verification. Most of the researches on signature verification are based on the real-stroke, or the trajectories that users write on a digitizer surface. However, forgery signatures written by skilled imitators usually challenge the performance of real-stroke-based verification systems. In this thesis, both feature extraction and verification methods are focus on the virtual-strokes. The virtual-strokes are the trajectories between each real-stroke in a signature. They are hard to be seen by naked eyes, but can be recorded by electronic devices. Due to this special characteristic of virtual-strokes, most of the forgery signatures can be easily identified. After the signature data has been acquired, each set of the one-dimensional signature data is converted into a two-dimensional signature image. Then, off-line signature verification methods are employed to accomplish the signature verification task. The developed system can prevent the occurring of forgery signatures to certain extend.

關鍵字(中)

★ 生物特徵
★ 簽名確認
★ 虛筆

關鍵字(英)

★ Biometrics
★ Signature Verification
★ Virtual-strokes

論文目次

Abstracti
摘要ii
目錄iii
附圖目錄v
表格目錄vi
第一章緒論1
1.1 研究動機1
1.2 相關研究5
1.3 系統流程7
1.4 論文架構10
第二章前處理11
2.1 資料取得11
2.2 雜訊去除12
2.3 正規化13
2.4 產生二維簽名影像13
還原原始簽名軌跡14
簽名影像的灰階值15
簽名速度16
筆劃寬度17
2.5 非等比例切割19
2.6 簽名影像外框21
第三章特徵抽取23
3.1 線上簽名特徵24
3.1.1 總簽名時間25
3.1.2 持筆角度25
3.2 二維簽名特徵28
3.2.1 方向性特徵28
第四章參考樣本建立與特徵比對31
4.1 線上簽名特徵比對32
4.1.1 總簽名時間33
4.1.2 持筆角度34
4.2 二維簽名特徵比對35
第五章實驗結果37
5.1 簽名資料庫建立37
5.1.1 真實簽名資料庫38
5.1.2 模仿簽名資料庫38
5.2 Pen-up/pen-down gaps 與Virtual-strokes的比較39
5.2.1 實驗40
5.2.2 討論41
5.3 以虛筆資訊為特徵抽取來源之簽名確認系統41
5.3.1 實驗41
5.3.2 討論48
5.4 二階簽名確認系統50
5.4.1 實驗51
5.4.2 討論55
第六章結論與未來工作57
參考文獻60

參考文獻

[1] Al-Yousefi, H., and S. S. Udpa, “Recognition of Arabic characters,” IEEE Trans. Pattern Analysis and Machine Intell. PAMI-14, 853-857, 1992.
[2] Brault, J. J., R. Plamondon, “A complexity measure of handwritten curves: Modeling of Dynamic Signature Forgery,” IEEE Trans. on SMC, Vol. 23, No. 2, 400-413, 1993
[3] Brault, J. J. and R. Plamondon, “Segmenting handwritten signatures at their perceptually important point,” IEEE Trans. Pattern Anal. Mach. Intell., Vol. 15, No. 9, 953-957, 1993.
[4] Bajaj, R. and S. Chaudhury, “Signature verification using multiple neural classifiers,” Pattern Recognition, Vol. 30, No. 1, 1-7, 1997.
[5] Cheng, N. J., K. Liu, K. C. Cheng, C. C. Tseng and B. S. Jeng, “On-line Chinese signature verification using voting scheme,” in Proceedings IEEE, International Carnaham Conference on Security Technology, 123-126, 1997.
[6] Cheng, N. J., C. J. Wen, H. F. Yau, D. H. Liu, K. Liu, K. C. Cheng and B. S. Jeng, “On-line Chinese signature verification with mixture of experts,” in Proceedings IEEE, International Carnahan Conference on Security Technology, 244-247, 1998.
[7] Cheng, N. J., C. J. Wen, H. F. Yau, T. S. Liu, K. Liu, K. C. Cheng and B. S. Jeng, “Handwritten Chinese signature verification based on attributed string matching of stroke linkage order,” in Proceedings IEEE, International Carnahan Conference on Security Technology, 238-243, 1998.
[8] Drouhard, J. P., R. Sabourin and M. Godbout, “A neural network approach to off-line signature verification using directional PDF,” Pattern Recognition, Vol. 29, No. 3, 415-424, 1996.
[9] Fairhurst, M. C., “New perspectives in automatic signature verification,” Information Security Technical Report, Vol. 3, No. 1, 52-59, 1998.
[10] Han, K. and I. K. Sethi, “Handwritten signature retrieval and identification,” Pattern Recognition Letters 17, 83-90, 1996.
[11] Huang, K. and H. Yan, “Off-line signature verification based on geometric feature extraction and neural network classification,” Pattern Recognition, Vol. 30, No. 1, 9-17, 1997.
[12] Lin, C. F. and C. W. Chen, “A new approach to the verification of Chinese signatures with variant orientations and scales using relaxation and state-space search methods,” Pattern Recognition, Vol. 31, No. 6, 665-674, 1998.
[13] Lin, C. F. and R. Z. Wang, “An accurate method of measuring similarities for Chinese signature verification system using Fourier descriptors,” ICS, 1998.
[14] Lecce, V. D., G. Dimauro, A. Guerriero, S. Impedovo, G. Pirlo, A. Salzo and L. Sarcinella, “Selection of reference signature for automatic signature verification,” in Proceedings of the 5th International Conference on Document Analysis and Recognition, 597-600, 1999.
[15] Munich, M. E. and P. Perona, “Visual signature verification using affine arc-length,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2, 180-186, 1999.
[16] Nalwa, V. S., “Automatic on-line signature verification,” Biometrics-Personal Identification in Networked Society, Kluwer Academic Publishers, 1999, Chapter 7.
[17] Plamondon, R. and G. Lorette, “Automatic signature verification and writer identification — the state of the art,” Pattern Recognition 22, 107-131, 1989.
[18] Parizeau, M. and R. Plamondan, “A comparative analysis of regional correlation, dynamic time warping, and skeletal tree matching for signature verification,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 12, No. 7, 710-717, 1990.
[19] Pavlidis, I., N. P. Papanikolopoulos and R. Mavuduru, “Signature identification through the use of deformable Structure,” Signal Processing 71, 187-201, 1998.
[20] Rigoll, G. and A. Kosmala, “A systematic comparison between on-line and off-line methods for signature verification with hidden Markov models,” in Proceedings IEEE, 14th International Conference on Pattern Recognition, Vol. 2, 1755-1757, 1998.
[21] Wu, Q. Z., S. Y. Lee and I. C. Jou, “On-line signature verification based on split-and-merge matching,” Pattern Recognition Letters 18, 665-673, 1997.
[22] Wu, Q. Z., I. C. Jou and S. Y. Lee, “On-line signature verification using LPC cepstrum and neural networks,” IEEE Trans. on SMC-Part B : Cybernetics, Vol. 27, No. 1, 148-153, 1997.
[23] Wu, Q. Z., S. Y. Lee and I. C. Jou, “On-line signature verification based on logarithmic spectrum,” Pattern Recognition, Vol. 31, No. 12, 1865-1871, 1998.
[24] Xuhua, Y., T. Furuhashi, K. Obata and Y. Uchikawa, “Constructing a high performance signature verification system using a GA method,” in Proceedings, Second New Zealand International Two-stream Conference on Artificial Neural Networks and Expert System, 170-173, 1995.
[25] Yang, L., B. K. Widjaja and R. Prasad, “Application of hidden Markov models for signature verification,” Pattern Recognition, Vol. 28, No. 2, 161-170, 1995.
[26] 李奇政, “利用離線簽名樣本做線上中文簽名確認,” 中央大學資訊工程研究所碩士論文, 1999.
[27] 周國森, “結構式特徵用於線上手寫中文文字的辨認”, 中央大學資訊工程研究所博士論文, 1997.
[28] 鄭旭良, “利用掌紋作個人身份之確認”, 中央大學資訊工程研究所碩士論文, 2000.

指導教授

范國清(Kuo-Chin Fan)

審核日期

2001-6-29

推文