臺灣博碩士論文加值系統

隨著科技的快速發展，資訊安全的重視也日趨重視，個人身分證驗系統也被廣泛運用在日常生活中，其中行動支付也開始慢慢崛起，各種生物特徵認證也漸漸的運用在行動支付上。本論文提出指靜脈辨識認證結合於QR Code上，它不需網路連結即可進行安全且快速的個人身分認證。本論文在指靜脈辨識部分是使用SURF與細線化擷取其特徵點，而為了能將龐大的SURF特徵資訊儲存到QR Code裡，本論文提出了主結構之感興趣區域(Main-Structure ROI)，對SURF特徵資訊進行資訊壓縮，只留下主架構之感興趣區域的專屬特徵資訊，排除掉部分主架構不像的指靜脈影像，因而減少了入侵的情況發生。另一方面，擷取指靜脈細線化後的岔點做為特徵點，將其存入QR Code。最後在驗證比對部分，為提升本論文系統之辨識率，本論文對SURF特徵點使用了相似性配對分數(Similarity Matching Score, SMS)而岔點特徵點採用權重全域配對分數(Weight Global Distance Score, WGDS)來進行雙重特徵驗證。

With the development of current technologies, information security has gotten much attention. Personal identity verification systems are also widely used in our daily life. The mobile payment systems are generated gradually; especially, the various biometric authentication systems.
In this thesis, we proposed a finger vein verification system based on QR code techniques without internet. We used Surf and Thinning techniques to extract the features from a finger vein. As for storing a huge number of features in QR code, we only extracted the features from the Main-Structure ROI, which can not only reduce the number of features but can prevent the system from intrusion. On the other hand, the bifurcations of the finger vein are also stored as features in QR code. The dual-biometric matching strategies were finally proposed to improve the recognition rate. That is, the Similarity Matching Score (SMS) is used for Surf features and the Weight Global Distance Score (WGDS) is used for bifurcation features.

第一章 緒論
1.1 研究動機與目的
1.2 相關研究與探討
1.3 論文章節安排
第二章 系統架構與流程
2.1 系統機構
2.1.1 手指靜脈辨識模組
2.1.2 QR Code掃描模組
2.2 系統流程
2.2.1 註冊流程
2.2.2 辨識與驗證流程
第三章 研究方法與步驟
3.1 靜脈辨識介紹
3.2 靜脈影像前處理
3.3 靜脈影像特徵擷取
3.4 靜脈影像特徵比對
3.4.1 鄰近特徵點篩選
3.4.2 SURF相似配對分數(Similarity Matching Score)
3.4.3 細線化權重全域配對分數(Weight Global Distance Score)
3.4.4 雙重驗證(Dual-Biometric Finger Vein Verification)
第四章 系統與實驗結果
4.1 開發環境
4.2 操作範例
4.2.1 註冊操作流程
4.2.2 指靜脈驗證操作流程
4.3 實驗結果
第五章 結論
5.1 研究成果
5.2 未來展望
參考文獻

[1]Sehasnainjot Singh and Nirvair Neeru, “Finger print fusion using Daubechies (Db1) wavelet transformation and quality measures,” 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom), pp. 822 - 826, 2015.
[2]Sheng-Hsun Hsieh, Yung-Hui Li, Chung-Hao Tien and Chin-Chen Chang, “Extending the capture volume of an iris recognition system using wavefront coding and super-resolution,” IEEE Transactions on Cybernetics, pp. 3342 - 3350, 2016.
[3]國家政策網路智庫, “運用生物特徵辨識身分制度之比較研究,” http://thinktank.nat.gov.tw/lp.asp?ctNode=146&CtUnit=7&BaseDSD=11&mp=1&htx_ghtx.topcat=18982.
[4]維基百科, “QR碼,” https://zh.wikipedia.org/wiki/QR%E7%A2%BC.
[5]Lin You, Hong Li and Jiawan Wang, “Finger-vein recognition algorithm based on potential energy theory,” 2015 IEEE 16th International Conference on Communication Technology (ICCT), pp. 678 - 683, 2015.
[6]Anjali Agarwal, Saurabh Maheshwari and Garima Yadav, “Vein biometric security using irreversible curve fitting accounting for minimum storage,” 2014 International Conference on Signal Propagation and Computer Technology (ICSPCT), pp. 179 - 183, 2014.
[7]Pengfei Ji, Yonghwa Kim, Yong Yang and Yoo-Sung Kim, “Face occlusion detection using skin color ratio and LBP features for intelligent video surveillance systems,” 2016 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 253 - 259, 2016.
[8]Wisarut Surakarin and Prabhas Chongstitvatana, “Predicting types of clothing using SURF and LDP based on Bag of Features,” 2015 12th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), pp. 1 - 5, 2015.
[9]Jun Fu, Lilue Fan and Zhiguo Yang, “Aircraft recognition in remote sensing images based on saliency and invariant moments,” 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), pp. 500 - 505, 2016.
[10]Yingying Li, Qingjie Liu, Linhai Jing, Shuo Liu and Fengxian Miao, “A genetic-optimized multi-angle normalized cross correlation SIFT for automatic remote sensing registration,” 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 2586 - 2589, 2016.
[11]Jialiang Peng, Ning Wang, Ahmed A. Abd El-Latif, Qiong Li and Xiamu Niu, “Finger-vein verification using Gabor filter and SIFT feature matching,” 2012 Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), pp. 45 - 48, 2012.
[12]Xuekui Yan, Feiqi Deng and Wenxiong Kang, “Palm vein recognition based on multi-algorithm and score-Level fusion,” 2014 Seventh International Symposium on Computational Intelligence and Design (ISCID), pp. 441 - 444, 2014.
[13]Daniel Hartung and Jesper Kückelhahn, “Dorsal finger texture recognition: investigating fixed-length SURF,” 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1315 - 1321, 2012.
[14]V. Gurunathan, T. Sathiyapriya and R. Sudhakar, “Multimodal biometric recognition system using SURF algorithm,” 2016 10th International Conference on Intelligent Systems and Control (ISCO), pp. 1 - 5, 2016.
[15]Onur Can Kurban, Özden Nıyaz and Tülay Yildirim, “Neural network based wrist vein identification using ordinary camera,” 2016 International Symposium on INnovations in Intelligent SysTems and Applications (INISTA), pp. 1 - 4, 2016.
[16]Renke Zhang, Di Huang and Yunhong Wang, “Textured detailed graph model for dorsal hand vein recognition: A holistic approach,” 2016 International Conference on Biometrics (ICB), pp. 1 - 7, 2016.
[17]Maleika Heenaye-Mamode Khan and Naushad Ali Mamode Khan, “Investigating linear discriminant analysis (LDA) on dorsal hand vein images,” 2013 Third International Conference on Innovative Computing Technology (INTECH), pp. 54 - 59, 2013.
[18]R. Raghavendra and Christoph Busch, “Exploring dorsal finger vein pattern for robust person recognition,” 2015 International Conference on Biometrics (ICB), pp. 341 - 348, 2015.
[19]J. Chavez-Galaviz, J. Ruiz-Rojas, A. Garcia-Gonzalez and RQ. Fuentes-Aguilar, “Embedded biometric cryptosystem based on finger vein patterns,” 2015 12th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), pp. 1 - 6, 2015.
[20]Alexandre Sierro, Pierre Ferrez and Pierre Roduit, “Contact-less palm/finger vein biometrics,” 2015 International Conference of the Biometrics Special Interest Group (BIOSIG), pp. 1 - 12, 2015.
[21]Masaki Watanabe, Toshio Endoh, Morito Shiohara and Shigeru Sasaki, “Palm vein authentication technology and its applications,” The Biometric Consortium Conference, pp. 1-2, 2005.
[22]Naoto Miura, Akio Nagasaka and Takafumi Miyatak, “Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification,” Machine Vision and Applications, pp. 194, 2004.
[23]M. Mohamed Syed Ibrahim, Faris Salman Al-Namiy, Marsaline Beno and L. Rajaji, “Biometric authentication for secured transaction using finger vein technology,” International Conference on Sustainable Energy and Intelligent Systems (SEISCON 2011), pp. 760 - 763, 2011.
[24]Souad Khellat-kihel, Reza abrishambaf, Nuno Cardoso, João Monteiro and Mohamed Benyettou, “Finger vein recognition using Gabor filter and support vector machine,” 2014 First International Image Processing, Applications and Systems Conference (IPAS), pp. 1 - 6, 2014.
[25]Jialiang Peng, Ning Wang, Ahmed A. Abd El-Latif, Qiong Li and Xiamu Niu, “Finger-vein verification using Gabor filter and SIFT feature matching,” 2012 Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), pp. 1758 - 1763, 2014.
[26]鄭又誠, “Apply bi-features to implement a palm vein recognition system and device based on Gabor-Min,” Master Thesis, Dept. of Comp. Sci. and Inf. Eng., National Taiwan University of Science and Technology, 2013.
[27]J.G. Daugman, “Two-dimensional spectral analysis of cortical receptive field profiles,” Vision Research, vol. 20, pp. 847-856, 1980.
[28]Woods, R.E. and Gonzalez, R.C., “Real-time digital image enhancement,” Proceedings of the IEEE, pp.683 - 654, 1981.
[29]Karel Zuiderveld, “Contrast limited adaptive histogram equalization,” Academic Press Professional, pp. 474 - 485, 1994.
[30]Stephen Se, David Lowe and Jim Little, “Vision-based mobile robot localization and mapping using scale-invariant features,” Int. Conf. on Robotics and Automation (ICRA), pp. 2051 - 2058, 2001.
[31]Herbert Bay, Tinne Tuytelaars, Luc Van Gool and ETH Zurich, “SURF: speeded up robust features,” Computer Vision and Image Understanding (CVIU), pp. 346 - 359, 2008.
[32]H. Bay, B. Fasel, and L. van Gool, “Interactive museum guide: fast and robust recognition of museum objects,” Proceedings of the First International Workshop on Mobile Vision, 2006.
[33]H. Bay, T. Tuytelaars and L. Van Gool, “SURF: speeded up robust features,” ECCV, pp. 404 - 417, 2006.
[34]Huang, Hui et al., “A new scale invariant feature detector and modified SURF descriptor,” 2010 Sixth International Conference on Natural Computation (ICNC), pp. 3734 - 3738, 2010.
[35]Simard, Patrice Y. et al., “Boxlets: a fast convolution algorithm for signal processing and neural networks,” Advances in Neural Information Processing Systems, pp. 571 - 577, 1999.
[36]Viola P. and Jones M., “Rapid object detection using a boosted cascade of simple features,” IEEE Conference on Computer Vision and Pattern Recognition, pp. I-511 - I-518, 2001.
[37]Hong Jiang, Shuxu Guo, Xueyan Li and Xiaohua Qian, “Vein pattern extraction based on the position-gray-profile curve,” 2009. CISP '09. 2nd International Congress on Image and Signal Processing, pp. 1 - 4, 2009.
[38]鐘國亮, “影像處理與電腦視覺,” 東華書局, 2006.
[39]M. E. Rettmann, M. S. Gunawan, D. R. Holmes, D. L. Packer and R. A. Robb, “Quantification of pulmonary vein morphology using centerline tracking,” 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), pp. 816 - 819, 2012.
[40]Jinpeng Deng and Yanzhi Zhang, “Hand veins feature extraction based on morphology and cellular neural network,” 2010 International Conference on Computer Application and System Modeling (ICCASM), pp. V14-175 - V14-178, 2010.
[41]Xiaoxia Li, Di Huang, Renke Zhang, Yunhong Wang and Xianbo Xie, “Hand dorsal vein recognition by matching width skeleton models,” 2016 IEEE International Conference on Image Processing (ICIP), pp. 3146 - 3150, 2016.
[42]Lin Yang, Xiangbin Liu and Zhicheng Liu, “A skeleton extracting algorithm for dorsal hand vein pattern,” 2010 International Conference on Computer Application and System Modeling (ICCASM), pp. V13-92 - V13-95. 2010.
[43]Di Cao, Jinfeng Yang, Yihua Shi and Chenghua Xu, “Structure feature extraction for finger-vein recognition,” 2013 2nd IAPR Asian Conference on Pattern Recognition (ACPR), pp. 567 - 571, 2013.
[44]林哲民, “Development and applications of two-stage fast finger vein recognition based on smart handheld device,” Master Thesis, Dept. of Comp. Sci. and Inf. Eng., National Taiwan University of Science and Technology, 2016.
[45]Emanuela Piciucco, Emanuele Maiorana, Christof Kauba, Andreas Uhl and Patrizio Campisi, “Cancelable biometrics for finger vein recognition,” 2016 First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE), pp. 1 - 5, 2016.
[46]Fateme Saadat and Mehdi Nasri, “A GSA-based method in human identification using finger vein patterns,” 2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC), pp. 102 - 106, 2016.