請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88537完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 吳文方 | zh_TW |
| dc.contributor.advisor | Wen-Fang Wu | en |
| dc.contributor.author | 沈庭緯 | zh_TW |
| dc.contributor.author | Ting-Wei Shen | en |
| dc.date.accessioned | 2023-08-15T16:44:31Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-08-15 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-08-01 | - |
| dc.identifier.citation | [1] A. F. Abate, M. Nappi, D. Riccio, and G. Sabatino. 2d and 3d face recognition: A survey. Pattern recognition letters, 28(14):1885–1906, 2007.
[2] H. H. Ahmed, H. M. Kelash, M. Tolba, and M. Badwy. Fingerprint image enhancement based on threshold fast discrete curvelet transform (fdct) and gabor filters. International Journal of Computer Applications, 110(3):33–41, 2015. [3] Z. Alqadi, M. Abuzalata, Y. Eltous, and G. M. Qaryouti. Analysis of fingerprint minutiae to form fingerprint identifier. JOIV: International Journal on Informatics Visualization, 4(1):10–15, 2020. [4] M. Altarawneh, L. C. Khor, W. L. Woo, and S. S. Dlay. A non reference fingerprint image validity via statistical weight calculation. Journal of Digital Information Management, 5(4), 2007. [5] A. I. Awad. Fast fingerprint orientation field estimation incorporating general purpose gpu. In Soft Computing Applications, pages 891–902. Springer, 2016. [6] A. M. Bazen and S. H. Gerez. Directional field computation for fingerprints based on the principal component analysis of local gradients. In Proceedings of ProRISC2000, 11th Annual Workshop on Circuits, Systems and Signal Processing, pages 215–222. Veldhoven, the Netherlands, 2000. [7] A. M. Bazen and S. H. Gerez. Systematic methods for the computation of the directional fields and singular points of fingerprints. IEEE transactions on pattern analysis and machine intelligence, 24(7):905–919, 2002. [8] J. Berry and D. A. Stoney. The history and development of fingerprinting. Advances in fingerprint Technology, 2:13–52, 2001. [9] W. Bian, D. Xu, Q. Li, Y. Cheng, B. Jie, and X. Ding. A survey of the methods on fingerprint orientation field estimation. IEEE Access, 7:32644–32663, 2019. [10] K. Cao and A. K. Jain. Automated latent fingerprint recognition. IEEE transactions on pattern analysis and machine intelligence, 41(4):788–800, 2018. [11] R. Cappelli. Fast and accurate fingerprint indexing based on ridge orientation and frequency. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 41(6):1511–1521, 2011. [12] K. Ciezar and M. Pochylski. 2d short-time fourier transform for local morphological analysis of meibomian gland images. Plos one, 17(6):e0270473, 2022. [13] S. Dyre and C. Sumathi. Reliable orientation field estimation of fingerprint based on adaptive neighborhood analysis. ICTACT Journal on Image & Video Processing, 7(3), 2017. [14] M. Galar, J. Derrac, D. Peralta, I. Triguero, D. Paternain, C. Lopez-Molina, S. García, J. M. Benítez, M. Pagola, and E. Barrenechea. A survey of fingerprint classification part ii: Experimental analysis and ensemble proposal. Knowledge-Based Systems, 81:98–116, 2015. [15] C. Gottschlich, P. Mihailescu, and A. Munk. Robust orientation field estimation and extrapolation using semilocal line sensors. IEEE Transactions on Information Forensics and Security, 4(4):802–811, 2009. [16] L. Hong, Y. Wan, and A. Jain. Fingerprint image enhancement: Algorithm and performance evaluation. IEEE transactions on pattern analysis and machine intelligence, 20(8):777–789, 1998. [17] U. S. F. B. o. Investigation. The Science of Fingerprints: Classification and Uses. US Department of Justice, Federal Bureau of Investigation, 1984. [18] A. Jain, A. Ross, and S. Prabhakar. Fingerprint matching using minutiae and texture features. In Proceedings 2001 International Conference on Image Processing (Cat. No. 01CH37205), volume 3, pages 282–285. IEEE, 2001. [19] A. K. Jain, A. Ross, and S. Prabhakar. An introduction to biometric recognition. IEEE Transactions on circuits and systems for video technology, 14(1):4–20, 2004. [20] H. Kekre and V. Bharadi. Fingerprint orientation field estimation algorithm based on optimized neighborhood averaging. In 2009 Second International Conference on Emerging Trends in Engineering & Technology, pages 228–234. IEEE, 2009. [21] H. Li, T. Wang, Y. Tang, J. Wu, P. Yu, L. Guo, J. Chen, and Y. Zhang. Combining multi-scale composite windows with hierarchical smoothing strategy for fingerprint orientation field computation. Biomedical engineering online, 17(1):1–21, 2018. [22] L.-m. Liu and T.-S. Dai. Ridge orientation estimation and verification algorithm for fingerprint enhancement. J. Univers. Comput. Sci., 12(10):1426–1438, 2006. [23] D. Maltoni, D. Maio, A. K. Jain, and S. Prabhakar. Handbook of fingerprint recognition. Springer Science & Business Media, 2009. [24] O. Marques. Practical image and video processing using MATLAB. John Wiley & Sons, 2011. [25] D. H. McMahon, G. L. Johnson, S. L. Teeter, and C. G. Whitney. A hybrid optical computer processing technique for fingerprint identification. IEEE Transactions on Computers, 100(4):358–369, 1975. [26] R. Medina, J. Llamas, J. Gómez-García-Bermejo, E. Zalama, and M. J. Segarra. Crack detection in concrete tunnels using a gabor filter invariant to rotation. Sensors, 17(7):1670, 2017. [27] Y. Mei, H. Sun, and D. Xia. A gradient-based combined method for the computation of fingerprints＇orientation field. Image and Vision Computing, 27(8):1169–1177, 2009. [28] D. Mukherjee and A. Das. Gabor Filter Based Automated Enhancement of Brain Tumors, pages 71–80. Springer, 2021. [29] E. Newham, C. Bunney, and C. Mearns. The biometrics report, sjb services. New York, pages 269–302, 1995. [30] M. d. A. Oliveira and N. J. Leite. A multiscale directional operator and morphological tools for reconnecting broken ridges in fingerprint images. Pattern Recognition, 41(1):367–377, 2008. [31] C. H. Park and H. Park. Fingerprint classification using fast fourier transform and nonlinear discriminant analysis. Pattern Recognition, 38(4):495–503, 2005. [32] Z. Qu, J. Liu, Y. Liu, Q. Guan, C. Yang, and Y. Zhang. Orienet: A regression system for latent fingerprint orientation field extraction. In International Conference on Artificial Neural Networks, pages 436–446. Springer, 2018. [33] K. Raja. Fingerprint recognition using minutia score matching. arXiv preprint arXiv:1001.4186, 2010. [34] N. K. Ratha, S. Chen, and A. K. Jain. Adaptive flow orientation-based feature extraction in fingerprint images. Pattern recognition, 28(11):1657–1672, 1995. [35] N. K. Ratha, K. Karu, S. Chen, and A. K. Jain. A real-time matching system for large fingerprint databases. IEEE transactions on pattern analysis and machine intelligence, 18(8):799–813, 1996. [36] R. P. Sharma and S. Dey. Fingerprint liveness detection using local quality features. The Visual Computer, 35(10):1393–1410, 2019. [37] F. Turroni, D. Maltoni, R. Cappelli, and D. Maio. Improving fingerprint orientation extraction. IEEE Transactions on Information Forensics and Security, 6(3):1002–1013, 2011. [38] Y. Wang, J. Hu, and F. Han. Enhanced gradient-based algorithm for the estimation of fingerprint orientation fields. Applied Mathematics and Computation, 185(2):823–833, 2007. [39] L. Wieclaw. Gradient based fingerprint orientation field estimation. Journal of Medical Informatics & Technologies, 22, 2013. [40] J. Yang, L. Liu, T. Jiang, and Y. Fan. A modified gabor filter design method for fingerprint image enhancement. Pattern Recognition Letters, 24(12):1805–1817, 2003. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88537 | - |
| dc.description.abstract | 在現今社會指紋識別技術已被廣泛應用於各種領域，包括門禁系統、法醫調查和海關管控等。然而低品質的指紋影像可能會影響指紋特徵識別的準確度。因此需要利用指紋影像增強技術來克服這個問題，比如Gabor濾波器就是最常見指紋影像增強技術，而Gabor濾波器需要可靠的方向場估算來確保影像增強的效果。在這篇論文中，我們介紹一種利用灰階強度微分值之指紋方向場估算法，並利用高斯模糊和高斯雜訊降低指紋影像品質，來檢驗所提出的演算法的準確性和可靠性。實驗結果顯示，在低品質的指紋影像中，本文所提演算法在指紋方向場估算的可靠性方面比基於梯度演算法和基於功率頻譜密度演算法更好。尤其是在有雜訊的指紋影像中，本文演算法指紋方向場估算可靠性比基於梯度演算法和基於功率頻譜密度演算法分別提高了6.46％和32.93％。 | zh_TW |
| dc.description.abstract | Fingerprint identification technologies are widely used for various applications, including access control systems, forensic investigations, and border security. However, low-quality fingerprint images may affect the accuracy of fingerprint identification. Therefore, fingerprint image enhancement, such as the Gabor filter, is necessary and the Gabor filter requires a reliable orientation field estimation to ensure the result of image enhancement. In this study, we introduce an orientation field estimation algorithm based on differential values of grayscale intensity and examine the accuracy and reliability of the proposed algorithm by applying it to fingerprint images processed using the Gaussian blurring and the Gaussian white noise process. The experimental results indicate that the orientation field estimation reliability of the proposed algorithm is higher than the gradient-based method and the power spectrum density-based method in low quality fingerprints. The proposed algorithm is especially useful in noisy fingerprint images, where the orientation field estimation reliability of the algorithm is 6.46% and 32.93% higher than the gradient-based method and the power spectrum density-based method, respectively. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-15T16:44:31Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-08-15T16:44:31Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Verification Letter from the Oral Examination Committee i
Acknowledgements iii 摘要 v Abstract vii Contents ix List of Figures xi List of Tables xiii Nomenclature xv Chapter 1 Introduction 1 1.1 Introduction 1 1.2 Related methods 8 1.2.1 The gradient-based method 8 1.2.2 The PSD-based method 9 Chapter 2 Methodology 13 2.1 Differential values of grayscale intensity in each orientation 13 2.2 Image partition 18 2.3 Image convolution 20 2.4 Orientation field estimation (index, matrices) 25 2.5 Image reconstruction 25 Chapter 3 Experiments and Results 29 3.1 Database 34 3.2 Experiment 1: Accuracy assessment in clear fingerprint images 34 3.3 Experiment 2: Accuracy assessment in blurred fingerprint images 39 3.4 Experiment 3: Accuracy assessment in noisy fingerprint images 46 Chapter 4 Conclusions 55 Chapter 5 Future Work 57 References 59 Appendix — Biography and Publication 65 .1 Biography 65 .2 Publication 65 | - |
| dc.language.iso | en | - |
| dc.subject | Gabor 濾波器 | zh_TW |
| dc.subject | 指紋方向場估算-梯度法 | zh_TW |
| dc.subject | 指紋 | zh_TW |
| dc.subject | 方向場估算 | zh_TW |
| dc.subject | Orientation Field Estimation | en |
| dc.subject | Fingerprint | en |
| dc.subject | The Gradient-based Method | en |
| dc.subject | The Gabor Filter | en |
| dc.title | 一種利用灰階強度微分值之指紋方向場估算法 | zh_TW |
| dc.title | An Effective Fingerprint Orientation Field Estimation Method Using Differential Values of Grayscale Intensity | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.oralexamcommittee | 楊宏智;丁肇隆;張恆華;張敬源;李敏鴻;李中裕 | zh_TW |
| dc.contributor.oralexamcommittee | Hong-Tsu Young;Chao-Lung Ting;Herng-Hua Chang;Ching-Yuan Chang;Min-Hung Lee;Jung-Yu Li | en |
| dc.subject.keyword | 指紋,方向場估算,指紋方向場估算-梯度法,Gabor 濾波器, | zh_TW |
| dc.subject.keyword | Fingerprint,Orientation Field Estimation,The Gradient-based Method,The Gabor Filter, | en |
| dc.relation.page | 66 | - |
| dc.identifier.doi | 10.6342/NTU202302201 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2023-08-04 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 機械工程學系 | - |
| 顯示於系所單位： | 機械工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-111-2.pdf | 10.13 MB | Adobe PDF | 檢視/開啟 |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。