指紋特徵點偵測擷取演算法與軟體高階合成

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：95

、訪客IP：18.117.102.227

姓名

廖御先(Yu-Hsien Liao) 查詢紙本館藏

畢業系所

資訊工程學系在職專班

論文名稱

指紋特徵點偵測擷取演算法與軟體高階合成
(Fingerprint Minutiae Detection and Extraction Algorithm with Software High-Level Modeling and Synthesis)

相關論文

★ 整合GRAFCET虛擬機器的智慧型控制器開發平台	★ 分散式工業電子看板網路系統設計與實作
★ 設計與實作一個基於雙攝影機視覺系統的雙點觸控螢幕	★ 智慧型機器人的嵌入式計算平台
★ 一個即時移動物偵測與追蹤的嵌入式系統	★ 一個固態硬碟的多處理器架構與分散式控制演算法
★ 基於立體視覺手勢辨識的人機互動系統	★ 整合仿生智慧行為控制的機器人系統晶片設計
★ 嵌入式無線影像感測網路的設計與實作	★ 以雙核心處理器為基礎之車牌辨識系統
★ 基於立體視覺的連續三維手勢辨識	★ 微型、超低功耗無線感測網路控制器設計與硬體實作
★ 串流影像之即時人臉偵測、追蹤與辨識─嵌入式系統設計	★ 一個快速立體視覺系統的嵌入式硬體設計
★ 即時連續影像接合系統設計與實作	★ 基於雙核心平台的嵌入式步態辨識系統

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2028-8-1以後開放)

摘要(中)

指紋特徵點擷取的精確度是影響指紋辨識的關鍵因素。本研究提出一個模組化的指紋特徵點擷取的軟體架構，利用MIAT方法論以及軟體高階合成的方法，將特徵點擷取的軟體設計成一個模組化的架構，使得不同指紋特徵擷取演算法都可以應用此架構來評估與測試。本研究使用啟發式方法、SIFT演算法與基於卷積神經網路的FingerNet，三種不同的方法來驗證此一軟體架構的可調性。實驗表明，使用者可以透過參數來切換不同的演算法，由於軟體模組使用正規化的輸入輸出格式，使得不同演算法可以在相同的軟體架構上進行精確性的性能評估。本研究成果有利於針對不同的指紋特性、不同取像環境、不同感測器特性，快速評估適用的指紋特徵點擷取演算法。

摘要(英)

The accuracy of fingerprint feature extraction is a critical factor affecting fingerprint recognition. This study proposes a modular software architecture for fingerprint feature extraction using the MIAT methodology and high-level software modeling and synthesis techniques. By using designed modular software framework, it allows different fingerprint feature extraction algorithms to be evaluated and tested within this architecture. Three distinct methods, Heuristic, Scale-Invariant Feature Transform (SIFT), and FingerNet, were used to verify the adaptability of the software architecture. Experimental results shows that users can switch between different algorithms by using different arguments of the software. The standardized input-output format enables accurate performance evaluation of different algorithms within the same software architecture. The findings of this study provide a convenient way to evaluate suitable fingerprint feature extraction algorithms for different fingerprint characteristics, imaging environments, and sensor properties.

關鍵字(中)

★ 指紋特徵點
★ 特徵點擷取
★ 軟體高階合成

關鍵字(英)

★ Fingerprint Minutiae
★ Minutiae Extraction
★ Software High-Level Modeling and Synthesis

論文目次

摘要 i
ABSTRACT ii
謝誌 iii
目錄 iv
圖目錄 vii
表目錄 x
第一章、緒論 1
1.1 研究背景 1
1.2 研究目的 3
1.3 論文架構 4
第二章、指紋特徵擷取的方法 5
2.1 指紋特徵 5
2.1.1 指紋特徵點的層級 6
2.1.2 指紋的特徵點 6
2.2 指紋特徵點擷取方法 7
2.3 SIFT特徵點擷取方法 9
2.4 SURF特徵點擷取方法 11
2.5 卷積神經網路指紋特徵點擷取方法 12
第三章、模組化指紋特徵點擷取系統設計 14
3.1 MIAT系統設計方法論 14
3.1.1 IDEF0 14
3.1.2 Grafcet 16
3.1.3 高階軟體合成 19
3.2 系統架構設計 19
3.2.1 A3模組化指紋特徵點擷取軟體 20
3.2.2 A31程式參數解析模組 21
3.2.3 A32指紋影像讀取模組 22
3.2.4 A33特徵點擷取函式庫載入模組 23
3.2.5 A34指紋特徵點擷取模組 23
3.3 離散事件建模 24
3.3.1 指紋辨識系統離散事件模型 24
3.3.2 模組化指紋特徵點擷取離散事件模型 25
3.3.3 解析程式參數離散事件模型 27
3.3.4 指紋影像讀取離散事件模型 28
3.3.5 函式庫讀取離散事件模型 29
3.3.6 函式庫選擇離散事件模型 30
3.3.7 特徵點擷取離散事件模型 31
3.4 軟體高階合成 33
3.5 演算法評估 35
第四章、實驗 37
4.1 實驗環境 37
4.2 函式庫整合實驗 38
4.3 特徵點擷取輸出實驗 42
4.4 指紋特徵點擷取實驗 43
4.4.1 啟發式方法 43
4.4.2 SIFT演算法的特徵點擷取方法 44
4.4.3 卷積神經網路的特徵點擷取方法 46
4.5 擷取結果比較 47
4.6 實驗結果與討論 52
第五章、結論與未來發展方向 53
5.1 結論 53
5.2 未來展望 54
參考文獻 55
附錄一高階軟體合成程式碼 60

參考文獻

[1] S. F. Ali, M. A. Khan and A. S. Aslam, "Fingerprint matching, spoof and liveness detection: classification and literature review," Frontiers of Computer Science, p. 15, 2021.
[2] S. Bakheet, A. Alqahtani, S. Alsubai and A. Binbusayyis, "Robust Fingerprint Minutiae Extraction and Matching Based on Improved SIFT Features," Applied Sciences, p. 6122, 7 2022.
[3] Y. Li-Qiang and G. Ling, "Feature Extraction of Fingerprint Image Based on Minutiae Feature Points," in 2012 International Conference on Computer Science and Service System, Nanjing, China, 2012.
[4] F. Alonso-Fernandez, J. Bigun, J. Fierrez, H. Fronthaler, K. Kollreider and J. Ortega-Garcia, "Fingerprint recognition," in Guide to Biometric Reference Systems and Performance Evaluation, London, UK, 2009.
[5] S. Bakheet and A. Al-Hamadi, "A Framework for Instantaneous Driver Drowsiness Detection Based on Improved HOG Features and Naïve Bayesian Classification," Brain Sciences, vol. 11, no. 2, p. 240, 2 2021.
[6] J. Cheng, J. Tian and H. Chen, "Fingerprint Minutiae Matching with Orientation and Ridge," Lecture Notes in Computer Science, pp. 351-358, 2004.
[7] R. Zhou, D. Zhong and J. Han, "Fingerprint Identification Using SIFT-Based Minutia Descriptors and Improved All Descriptor-Pair Matching," Sensors, p. 3142–3156, 3 2013.
[8] A. Takahashi, Y. Koda, K. Ito and T. Aoki, "Fingerprint Feature Extraction by Combining Texture, Minutiae, and Frequency Spectrum Using Multi-Task CNN," in 2020 IEEE International Joint Conference on Biometrics (IJCB), Houston, TX, USA, 2020.
[9] R. Li, D. Song, Y. Liu and J. Feng, "Learning Global Fingerprint Features by Training a Fully Convolutional Network with Local Patches," in 2019 International Conference on Biometrics (ICB), Crete, Greece, 2019.
[10] S. Yelin , Z. Zhang, S. Liu and M. Liu, "Towards More Accurate Matching of Contactless Fingerprints With a Deep Geometric Graph Convolutional Network," IEEE Transactions on Biometrics, Behavior, and Identity Science, vol. 1, no. 5, pp. 29-38, 1 2023.
[11] Y. Shi, Z. Zhang, S. Liu and M. Liu, "Towards More Accurate Matching of Contactless Fingerprints With a Deep Geometric Graph Convolutional Network," IEEE Transactions on Biometrics, Behavior, and Identity Science, vol. 5, no. 1, pp. 29-38, 1 2023.
[12] V. H. Nguyen, J. Liu, T. H. B. Nguyen and H. Kim, "Universal fingerprint minutiae extractor using convolutional neural networks," IET Biometrics, pp. 47-57, 3 2020.
[13] N. Sasuga, K. Ito and T. Aoki, "Fingerprint Feature Extraction Using CNN with Multiple Attention Mechanisms," in 2022 IEEE International Joint Conference on Biometrics (IJCB), Abu Dhabi, United Arab Emirates, 2022.
[14] Y. Sun, Y. Tang and X. Chen , "A Neural Network-Based Partial Fingerprint Image Identification Method for Crime Scenes," Applied Sciences, p. 1188, 1 2023.
[15] J. Ivers, R. L. Nord, I. Ozkaya, C. Seifried, C. S. Timperley and M. Kessentini, "Industry′s Cry for Tools that Support Large-Scale Refactoring," in 2022 IEEE/ACM 44th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), Pittsburgh, PA, USA, 2022.
[16] J. Ivers, C. Seifried and I. Ozkaya, "Untangling the Knot: Enabling Architecture Evolution with Search-Based Refactoring," in 2022 IEEE 19th International Conference on Software Architecture (ICSA), Honolulu, HI, USA, 2022.
[17] C.-H. Chen, C.-Y. Chen and T.-M. Hsu, "Singular Points Detection in Fingerprints Based on Poincare Index and Local Binary Patterns," Journal of Imaging Science and Technology, vol. 63, pp. 030401-1-7, May-June 2019.
[18] C. Jin and H. Kim, "Pixel-level singular point detection from multi-scale Gaussian filtered orientation field," Pattern Recognition, vol. 43, no. 11, pp. 3879-3890, 2010.
[19] X. Yin, Y. Zhu and J. Hu, "Contactless Fingerprint Recognition Based on Global Minutia Topology and Loose Genetic Algorithm," IEEE Transactions on Information Forensics and Security, vol. 15, pp. 28-41, 2020.
[20] H. Chen, R. Ma and M. Zhang, "Recent Progress in Visualization and Analysis of Fingerprint Level 3 Features," ChemistryOpen, 11 2022.
[21] D. Maltoni, D. Maio, A. K. Jain and J. Feng, Handbook of Fingerprint Recognition, Third ed., Springer Nature Switzerland AG, 2022.
[22] R. Kaur, P. S. Sandhu and A. Kamra, "A novel method for fingerprint feature extraction," in 2010 International Conference on Networking and Information Technology, Manila, Philippines, 2010.
[23] D. G. Lowe, "Object recognition from local scale-invariant features," in Proceedings of the Seventh IEEE International Conference on Computer Vision, Kerkyra, Greece, 1999.
[24] H. Bay, T. Tuytelaars and L. V. Gool, "Speeded-Up Robust Features (SURF)," Lecture notes in computer science, vol. 3951, p. 404, 2006.
[25] S. Bakheet, A. Al-Hamadi and R. Youssef, "A Fingerprint-Based Verification Framework Using Harris and SURF Feature Detection Algorithms," Applied Sciences, vol. 12, no. 4, p. 2028, 2 2022.
[26] D. Dai, "An Introduction of CNN: Models and Training on Neural Network Models," in 2021 International Conference on Big Data, Artificial Intelligence and Risk Management (ICBAR), Shanghai, China, 2021.
[27] K. O′Shea and R. Nash, "An Introduction to Convolutional Neural Networks," arXiv, 2015.
[28] Y. Tang, F. Gao, J. Feng and Y. Liu, "FingerNet: An unified deep network for fingerprint minutiae extraction," in 2017 IEEE International Joint Conference on Biometrics (IJCB), Denver, CO, USA, 2017.
[29] L. N. Darlow and B. Rosman, "Fingerprint minutiae extraction using deep learning," in 2017 IEEE International Joint Conference on Biometrics (IJCB), Denver, CO, USA, 2017.
[30] C.-H. Chen, C.-S. An and C.-Y. Chen, "Fingerprint Quality Assessment Based on Texture and Geometric Features," Journal of Imaging Science and Technology, vol. 64, pp. 40403-1-40403-7(7), July 2020.
[31] C.-H. Chen, M.-Y. Lin and X.-C. Guo, "High-level Modeling and Synthesis of Smart Sensor Networks for Industrial Internet of Things," Computers & Electrical Engineering, vol. 61, p. 48–66, July 2017.
[32] C.-H. Chen and C.-T. Liu, "Person Re-Identification Microservice over Artificial Intelligence Internet of Things Edge Computing Gateway," Electronics, vol. 10, p. 2264, 9 2021.
[33] "FVC2004: The Third International Fingerprint Verification Competition," [Online]. Available: http://bias.csr.unibo.it/fvc2004/.

指導教授

陳慶瀚(Ching-Han Chen)

審核日期

2023-7-17

推文