Basic Search / Detailed Display

Author: 張富貿
Fu-Mao Chang
Thesis Title: 基於深度學習方法的非接觸式掌靜脈辨識系統
A Contactless Palm Vein Recognition System Based on Deep Learning Method
Advisor: 洪西進
Shi-Jinn Horng
Committee: 謝仁偉
Jen-Wei Hsieh
Cheng-An Yen
Degree: 碩士
Department: 電資學院 - 資訊工程系
Department of Computer Science and Information Engineering
Thesis Publication Year: 2020
Graduation Academic Year: 108
Language: 中文
Pages: 47
Keywords (in Chinese): 生物辨識掌靜脈辨識非接觸式系統UVC 鏡頭
Keywords (in other languages): Biometrics, Palm Vein Identification, Contactless System, UVC Camera
Reference times: Clicks: 462Downloads: 0
School Collection Retrieve National Library Collection Retrieve Error Report


The rapid development of science and technology in recent years has made people live more and more convenient. Relatively, people pay much attention on information security and identity recognition systems are gradually applied in various related environments. Biometrics is a hot research topic in recent years. Therefore, using the uniqueness of biometrics as an identity recognition system has gradually become one of the research directions in the field of computer science.
Since palm vein has the characteristics of living body information and the structure of human vein information is stable after grown up, palm vein is suitable for biometric identification system. However, the image capturing method of the previous palm vein recognition system is placed on a fixed mechanism, which is very easily influenced by the displacement of user's palm position. In this thesis, we use a contactless method to capture the palm vein images. It is more difficult than the fixed mechanism. How to effectively reduce the impact of the palm displacement, distance changing, and rotation caused by the user will be the key factors to determine the performance of this recognition system.
In this thesis, the identification system is divided into two stages. In the first stage, the captured palm vein image is binaried and the region of interest is obtained. Then in the second stage, the image of the region of interest is put into the deep learning network for training. Finally, a prepared test dataset is used to do testing and the best recognition results are obtained.

摘要 I ABSTRACT II 致謝 III 目錄 IV 圖目錄 VI 第一章 緒論 1 1.1 研究動機與目的 1 1.2 相關研究回顧 1 1.3 論文章節介紹 2 第二章 系統架構 4 2.1 系統機構 4 2.2 系統流程 5 (一) 系統註冊機制(System Registration) 5 (二) 系統辨識流程(System Recognition) 5 第三章 掌靜脈抓取ROI前處理 7 3.1 二值化前處理(PRE-BINARIZATION) 8 3.2 CONTRAST LIMITED ADAPTIVE HISTOGRAM EQUALIZATION (CLAHE) 9 3.3 全域二值化(SIMPLE TRESHOLDING) 11 3.4 開運算 12 3.5 抓取輪廓 14 3.6 找尋手指間谷點 19 3.7 用特徵點框ROI 25 3.8 小結(SUMMARY) 26 第四章 深度學習介紹 27 4.1 殘差深度網路(RESIDUAL NEURAL NETWORK) 27 4.2 殘差網路的改進 27 4.3 殘差網路架構 29 4.4 梯度最佳解演算法(OPTIMIZED ALGORITHM OF GRADIENT) 31 4.5 損失函數(LOSS FUNCTION) 35 4.6 小結 36 第五章 實驗結果 37 5.1 系統開發環境 37 5.2 系統執行畫面 37 5.3 掌靜脈資料庫 40 第六章 結論 42 第七章 未來展望 43 參考文獻 44

[1] Z. X. Geng and Y. Q. Qiao, "An Improved Illumination Invariant SURF Image Feature Descriptor," 2017 International Conference on Virtual Reality and Visualization (ICVRV), Zhengzhou, China, 2017, pp. 389-390.
[2] V. Gurunathan, T. Sathiyapriya and R. Sudhakar, "Multimodal biometric recognition system using SURF algorithm," 2016 10th International Conference on Intelligent Systems and Control (ISCO), Coimbatore, 2016, pp. 1-5.
[3] L. Zhu, "Finger knuckle print recognition based on SURF algorithm," 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), Shanghai, 2011, pp. 1879-1883.
[4] Zhang, Y.-B., Li, Q., You, J., et al., "Palm vein extraction and matching for personal authentication," Int. Conf. on Advances in Visual Information Systems, Shanghai, China, June 2007, pp. 154–164.
[5] Greitans, M., Pudzs, M., Fuksis, R., "Palm vein biometrics based on infrared imaging and complex matched filtering," Proc. of the 12th ACM Workshop on Multimedia and Security, Roma, Italy, September 2010, pp. 101–106.
[6] Kabacinski, R., Kowalski, M., "Vein pattern database and benchmark results," Electron. Lett., 2011, 47, (20), pp. 1127–1128.
[7] Dandawate, Y.H., Inamdar, S.R., "Fusion based multimodal biometric cryptosystem," 2015 Int. Conf. on Industrial Instrumentation and Control (ICIC), Wellesely Road, Shivaji Nagar, Pune, Maharashtra, India, May 2015, pp. 1484–1489.
[8] Gohk, O., Andrew, T., "Design and implementation of a contactless palm vein recognition system," Proc. of the 2010 Symp. on Information and Communication Technology, Hanoi, Vietnam, August 2010, pp. 92–99.
[9] P. Tome and S. Marcel, "Palm Vein Database and Experimental Framework for Reproducible Research," 2015 International Conference of the Biometrics Special Interest Group (BIOSIG), Darmstadt, 2015, pp. 1-7.
[10] P. Cancian, G. W. Di Donato, V. Rana and M. D. Santambrogio, "An embedded Gabor-based palm vein recognition system," 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), Orlando, FL, 2017, pp. 405-408.
[11] X. Yan, F. Deng and W. Kang, "Palm Vein Recognition Based on Multi-algorithm and Score-Level Fusion," 2014 Seventh International Symposium on Computational Intelligence and Design, Hangzhou, 2014, pp. 441-444.
[12] Jen-Chun Lee, "A novel biometric system based on palm vein image, " Pattern Recognition Letters, Volume 33, Issue 12, 2012, Pages 1520-1528.
[13] R. Movchan and Z. Shen, "Adaptive thresholding hosvd algorithm with iterative regularization for image denoising," 2017 IEEE International Conference on Image Processing (ICIP), Beijing, 2017, pp. 2991-2995.
[14] D. Liu and J. Yu, "Otsu Method and K-means," 2009 Ninth International Conference on Hybrid Intelligent Systems, Shenyang, 2009, pp. 344-349.
[15] S. M. Pizer, R. E. Johnston, J. P. Ericksen, B. C. Yankaskas and K. E. Muller, "Contrast-limited adaptive histogram equalization: speed and effectiveness," [1990] Proceedings of the First Conference on Visualization in Biomedical Computing, Atlanta, GA, USA, 1990, pp. 337-345.
[16] Karel Zuiderveld, "Contrast Limited Adaptive Histogram Equalization, " Graphics Gems IV(1994) , pp. 474-485.
[17] A. Mishra, M. Gupta and P. Sharma, "Enhancement of Underwater Images using Improved CLAHE," 2018 International Conference on Advanced Computation and Telecommunication (ICACAT), Bhopal, India, 2018, pp. 1-6.
[18] M. Mohan, L. P. T and L. S. Nair, "Fuzzy c-means Segmentation on Enhanced Mammograms Using CLAHE and Fourth Order Complex Diffusion," 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 2020, pp. 647-651.
[19] Satoshi Suzuki, KeiichiA be, "Topological structural analysis of digitized binary images by border following," Computer Vision, Graphics, and Image Processing, Volume 30, Issue 1, 1985, Pages 32-46.
[20] Lie Kang, Sheng Zhong and Fang Wang, "A new contour tracing method in a binary image," 2011 International Conference on Multimedia Technology, Hangzhou, 2011, pp. 6183-6186.
[21] Jayaram, M., Fleyeh, H. (2016), "Convex Hulls in Image Processing: A Scoping Review, " American Journal of Intelligent Systems, 6(2): pp. 48-58.
[22] Ren Lisheng, Wang Lizhong, "Analysis of image matching algorithm for corner detection based on curvature scale space, " Application of Electronic Technique, 2016, 42(12):pp. 112-114,118.
[23] "直方圖均化維基百科",
[24] "Adaptive histogram equalization Wikipedia",
[25] K. He, X. Zhang, S. Ren and J. Sun, "Deep Residual Learning for Image Recognition," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, 2016, pp. 770-778.
[26] M. Yusoh, A. Phon-on and K. Khongkraphan, "Estimating Motion Blur Parameters with Gradient Descent Method," 2018 22nd International Computer Science and Engineering Conference (ICSEC), Chiang Mai, Thailand, 2018, pp. 1-4.
[27] A. Bouillard and P. Jacquet, "Quasi Black Hole Effect of Gradient Descent in Large Dimension: Consequence on Neural Network Learning," ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, United Kingdom, 2019, pp. 8365-8369.
[28] G. Cong and O. Bhardwaj, "A Hierarchical, Bulk-Synchronous Stochastic Gradient Descent Algorithm for Deep-Learning Applications on GPU Clusters," 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), Cancun, 2017, pp. 818-821.
[29] R. G. J. Wijnhoven and P. H. N. de With, "Fast Training of Object Detection Using Stochastic Gradient Descent," 2010 20th International Conference on Pattern Recognition, Istanbul, 2010, pp. 424-427.
[30] R. R. Tobias et al., "Faster R-CNN Model With Momentum Optimizer for RBC and WBC Variants Classification," 2020 IEEE 2nd Global Conference on Life Sciences and Technologies (LifeTech), Kyoto, Japan, 2020, pp. 235-239.
[31] W. Xiong et al., "Convolutional Neural Network Based Atmospheric Turbulence Compensation for Optical Orbital Angular Momentum Multiplexing," in Journal of Lightwave Technology, vol. 38, no. 7, pp. 1712-1721, 1 April1, 2020.
[32] N. Zhang, D. Lei and J. F. Zhao, "An Improved Adagrad Gradient Descent Optimization Algorithm," 2018 Chinese Automation Congress (CAC), Xi'an, China, 2018, pp. 2359-2362.
[33] Diederik P. Kingma, Jimmy Ba, "Adam: A Method for Stochastic Optimization," 3rd International Conference for Learning Representations, San Diego, 2015
[34] "熵維基百科",
[35] "相對熵維基百科",
[36] "交叉熵維基百科",
[37] The Chinese Academy of Sciences, Automation Institute , "CASIA Palmprint Database, "
[38] The Hong Kong Polytechnic University , "PolyU Multispectral Palmprint Database, "

無法下載圖示 Full text public date 2022/08/27 (Intranet public)
Full text public date 2025/08/27 (Internet public)
Full text public date 2025/08/27 (National library)