基於抗混疊演算法和注意力機制的指靜脈認證技術

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姓名	陳功毅(Tran Cong Nghi) 查詢紙本館藏	畢業系所	資訊工程學系
論文名稱	基於抗混疊演算法和注意力機制的指靜脈認證技術 (Finger Vein Authentication Technique using Anti-Aliasing Algorithm and Attention Mechanisms)
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摘要(中)	本論文研究了指靜脈識別系統，該系統採用了一種安全的生物特徵識別方法，由於其固有的防偽性和非侵入式的獲取過程，使其成為可靠的選擇。論文引入了一種優化的指靜脈識別方法，該方法利用深度學習，著重於抗鋸齒技術和注意力機制的整合，以提高識別精度。研究分為三個主要部分。首先，我們開發了一種針對指靜脈識別的卷積神經網絡（CNN）架構，其中包含了旨在減少失真並保留靜脈圖案細節的新型抗鋸齒濾波器。其次，我們整合了注意力機制，特別是通道和空間注意力，這些機制選擇性地增強了對於靜脈圖案辨識至關重要的特徵。這種雙重方法同時解決了由於鋸齒現象而造成的細節損失和在復雜圖像中突出相關特徵的挑戰。在多個基準數據集上進行的廣泛實驗證明了所提出方法相比傳統指靜脈識別系統的優越性。我們的結果顯示了在識別精度和對各種圖像質量問題的魯棒性方面的改進，確認了在深度學習模型中結合抗鋸齒技術和注意力機制的有效性。本工作不僅推進了指靜脈識別技術的發展，還提供了可能適用於其他生物安全領域的洞見。
摘要(英)	This thesis is a study of the finger vein authentication system, utilizing a secure biometric method due to its intrinsic resistance to forgery and its non-intrusive acquisition process. It introduces an optimized approach for finger vein authentication using deep learning, focusing on the integration of anti-aliasing techniques and attention mechanisms to enhance authentication accuracy. The research is divided into three main parts. First, we develop a convolutional neural network (CNN) architecture tailored for finger vein recognition, incorporating novel anti-aliasing filters designed to mitigate distortion and preserve vein pattern details. Second, we integrate attention mechanisms, specifically channel and spatial attention, which selectively enhance features relevant for vein pattern discrimination. This dual approach addresses both the loss of detail due to aliasing and the challenge of emphasizing relevant features in complex images. Extensive experiments conducted on several benchmark datasets demonstrate the superiority of the proposed method over traditional finger vein authentication systems. Our results show improvements in recognition accuracy and robustness against various image quality issues, confirming the effectiveness of combining anti-aliasing techniques and attention mechanisms in deep learning models. This work not only advances the state of finger vein authentication but also offers insights that could be applied to other areas of biometric security.
關鍵字(中)	★ 指靜脈識別 ★ 深度學習 ★ 抗鋸齒 ★ 注意力機制 ★ 生物安全	關鍵字(英)	★ Finger Vein Authentication ★ Deep Learning ★ Anti-Aliasing ★ Attention Mechanisms ★ Biometric Security
論文目次	Chapter 1 Introduction 1 1.1 Overview 1 1.2 Related works 5 1.2.1 Conventional Approaches 5 1.2.2 Deep Learning Techniques 6 1.2.3 Future Direction 8 1.3 Research Objectives and Contributions 9 1.4 Thesis Structure 10 Chapter 2 The Finger vein Verification System 13 2.1 Introduction of finger vein verification system 13 2.2 The biometric definition of finger vein features 14 2.3 The process of finger vein image acquisition 16 2.3.1 Light reflection method 16 2.3.2 Light transmission method 17 2.4 Finger vein image preprocessing 18 2.4.1 Region of Interest (ROI) Segmentation in Finger Vein Recognition 18 2.4.2 Image Enhancement in Finger Vein verification 19 2.5 Finger vein feature extraction 21 2.5.1 Vein pattern-based methods 21 2.5.2 Dimensionality reduction-based methods 22 2.6 Finger vein matching 22 2.7 Conclusion 23 Chapter 3 Anti-Aliasing Convolution Neural Network of Finger Vein Recognition for Virtual Reality (VR) Human-Robot Equipment of Metaverse 25 3.1 Introduction 25 3.2 Methodology 26 3.2.1 Proposed system architecture 26 3.2.2 Preprocessing 28 3.2.3 Anti-aliasing convolution neural networks 29 3.2.4 Loss function 30 3.3 Hardware device 30 3.3.1 Virtual Reality (VR) Human-Robot equipment of Metaverse 31 3.3.2 The verification device of finger vein identification 34 3.4 Experiments 35 3.4.1 Datasets 35 3.4.2 Experimental configuration 36 3.4.3 Experimental results 38 3.5 Conclusion 39 Chapter 4 Zero-FVein Net: Optimizing Finger Vein Recognition with Shallow CNNs and Zero-Shuffle Attention for Low-Computational Devices 41 4.1 Introduction 41 4.2 Methodology 43 4.2.1 Shallow CNN network with a re-parameterization mechanism. 45 4.2.2 ZeroBlur-DBB module: diverse branch block with blur pool and zero shuffle coordinate attention. 47 4.2.3 Evaluation metrics and loss function 54 4.3 Experiments 55 4.3.1 Finger-vein datasets 55 4.3.2 Experimental configuration 56 4.3.3 Experimental result 57 4.3.4 Ablation study 58 4.4 Conclusion 59 Chapter 5 Results and Discussion 61 5.1 Comprehensive Analysis of Chapter 3 61 5.2 Comprehensive Analysis of Chapter 4 62 5.3 Analysis of Themes and Innovations 63 5.3.1 Technological Innovations: 63 5.3.2 Performance and Evaluation: 64 5.3.3 Future Directions: 65 Chapter 6 Conclusion and Future Works 67
參考文獻	[1] Jain, A.K., A. Ross, and S. Prabhakar, An introduction to biometric recognition. IEEE Transactions on circuits and systems for video technology, 2004. 14(1): p. 4-20. [2] Saini, R. and N. Rana, Comparison of various biometric methods. International Journal of Advances in Science and Technology, 2014. 2(1): p. 24-30. [3] Gupta, P. and P. Gupta, An accurate finger vein based verification system. Digital Signal Processing, 2015. 38: p. 43-52. [4] O′shea, K. and R. Nash, An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458, 2015. [5] Chaman, A. and I. Dokmanic. Truly shift-invariant convolutional neural networks. in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021. [6] Niu, Z., G. Zhong, and H. Yu, A review on the attention mechanism of deep learning. Neurocomputing, 2021. 452: p. 48-62. [7] Miura, N., A. Nagasaka, and T. Miyatake, Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification. Machine vision and applications, 2004. 15: p. 194-203. [8] Miura, N., A. Nagasaka, and T. Miyatake, Extraction of finger-vein patterns using maximum curvature points in image profiles. IEICE TRANSACTIONS on Information and Systems, 2007. 90(8): p. 1185-1194. [9] Huang, B., et al. Finger-vein authentication based on wide line detector and pattern normalization. in 2010 20th international conference on pattern recognition. 2010. IEEE. [10] Yang, J. and Y. Shi, Towards finger-vein image restoration and enhancement for finger-vein recognition. Information Sciences, 2014. 268: p. 33-52. [11] Ma, H., N. Hu, and C. Fang, The biometric recognition system based on near-infrared finger vein image. Infrared Physics & Technology, 2021. 116: p. 103734. [12] Kumar, A. and Y. Zhou, Human identification using finger images. IEEE Transactions on image processing, 2011. 21(4): p. 2228-2244. [13] Meng, X., et al., Finger vein recognition based on zone-based minutia matching. Neurocomputing, 2021. 423: p. 110-123. [14] Hong, H.G., M.B. Lee, and K.R. Park, Convolutional neural network-based finger-vein recognition using NIR image sensors. Sensors, 2017. 17(6): p. 1297. [15] Zeng, J., et al., Finger vein verification algorithm based on fully convolutional neural network and conditional random field. IEEE access, 2020. 8: p. 65402-65419. [16] Yang, W., et al., FV-GAN: Finger vein representation using generative adversarial networks. IEEE Transactions on Information Forensics and Security, 2019. 14(9): p. 2512-2524. [17] Hou, B. and R. Yan, Triplet-classifier GAN for finger-vein verification. IEEE Transactions on instrumentation and measurement, 2022. 71: p. 1-12. [18] Kuzu, R.S., E. Maiorana, and P. Campisi. Vein-based biometric verification using transfer learning. in 2020 43rd International Conference on Telecommunications and Signal Processing (TSP). 2020. IEEE. [19] Wang, Y., D. Shi, and W. Zhou, Convolutional neural network approach based on multimodal biometric system with fusion of face and finger vein features. Sensors, 2022. 22(16): p. 6039. [20] Zhao, D., et al., Finger vein recognition based on lightweight CNN combining center loss and dynamic regularization. Infrared Physics & Technology, 2020. 105: p. 103221. [21] Ding, X., et al. Diverse branch block: Building a convolution as an inception-like unit. in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021. [22] Hou, Q., D. Zhou, and J. Feng. Coordinate attention for efficient mobile network design. in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021. [23] Xie, S.J., et al., Intensity variation normalization for finger vein recognition using guided filter based singe scale retinex. Sensors, 2015. 15(7): p. 17089-17105. [24] Hashimoto, J. Finger vein authentication technology and its future. in 2006 Symposium on VLSI Circuits, 2006. Digest of Technical Papers. 2006. IEEE. [25] Syazana-Itqan, K., et al., A review of finger-vein biometrics identification approaches. Indian Journal of Science and Technology, 2016. [26] Nguyen, D.T., et al., Spoof detection for finger-vein recognition system using NIR camera. Sensors, 2017. 17(10): p. 2261. [27] Qin, B., et al. The anti-spoofing study of vein identification system. in 2009 international conference on computational intelligence and security. 2009. IEEE. [28] Guan, F., K. Wang, and Q. Wu. Bi-directional weighted modular b2dpca for finger vein recognition. in 2010 3rd International Congress on Image and Signal Processing. 2010. IEEE. [29] Kutemate, S. and R. Shekokar, Secure and reliable human identification based on finger-vein patterns. Int J Eng Res Technol, 2015. 4(3): p. 2278-0181. [30] Lee, E.C., H.C. Lee, and K.R. Park, Finger vein recognition using minutia‐based alignment and local binary pattern‐based feature extraction. International Journal of Imaging Systems and Technology, 2009. 19(3): p. 179-186. [31] Mohammadi, P., A. Ebrahimi-Moghadam, and S. Shirani, Subjective and objective quality assessment of image: A survey. arXiv preprint arXiv:1406.7799, 2014. [32] Wang, Z., Applications of objective image quality assessment methods [applications corner]. IEEE signal processing magazine, 2011. 28(6): p. 137-142. [33] Rosdi, B.A., C.W. Shing, and S.A. Suandi, Finger vein recognition using local line binary pattern. Sensors, 2011. 11(12): p. 11357-11371. [34] Yang, J. and X. Li. Efficient finger vein localization and recognition. in 2010 20th International Conference on Pattern Recognition. 2010. IEEE. [35] Yang, J. and Y. Shi, Finger–vein ROI localization and vein ridge enhancement. Pattern Recognition Letters, 2012. 33(12): p. 1569-1579. [36] Yang, G., et al., Finger vein recognition based on personalized weight maps. Sensors, 2013. 13(9): p. 12093-12112. [37] Yusoff, S., et al., Review on vein enhancement methods for biometric system. Int. J. Res. Eng. Technol, 2015. 4(04): p. 833-841. [38] Arun, R., et al., An alpha rooting based hybrid technique for image enhancement. image, 2011. 9(10): p. 1-10. [39] Agaian, S.S., B. Silver, and K.A. Panetta, Transform coefficient histogram-based image enhancement algorithms using contrast entropy. IEEE transactions on image processing, 2007. 16(3): p. 741-758. [40] Tang, J., E. Peli, and S. Acton, Image enhancement using a contrast measure in the compressed domain. IEEE signal processing LETTERS, 2003. 10(10): p. 289-292. [41] Gupta, A. and Y. Kaushik, Comparative study of noise removal techniques. Int J Curr Eng Technol, 2014. 4(6): p. 3904-3907. [42] Kang, W., et al., Contact-free palm-vein recognition based on local invariant features. PloS one, 2014. 9(5): p. e97548. [43] Syarif, M.A., et al., Enhanced maximum curvature descriptors for finger vein verification. Multimedia Tools and Applications, 2017. 76: p. 6859-6887. [44] Khellat-Kihel, S., et al. Finger vein recognition using Gabor filter and support vector machine. in International image processing, applications and systems conference. 2014. IEEE. [45] Song, W., et al., A finger-vein verification system using mean curvature. Pattern Recognition Letters, 2011. 32(11): p. 1541-1547. [46] Qin, H., L. Qin, and C. Yu, Region growth–based feature extraction method for finger-vein recognition. Optical Engineering, 2011. 50(5): p. 057208-057208-8. [47] Yang, L., et al., Exploring soft biometric trait with finger vein recognition. Neurocomputing, 2014. 135: p. 218-228. [48] Liu, T., et al., An algorithm for finger-vein segmentation based on modified repeated line tracking. The Imaging Science Journal, 2013. 61(6): p. 491-502. [49] Vlachos, M. and E. Dermatas, Finger vein segmentation from infrared images based on a modified separable mumford shah model and local entropy thresholding. Computational and mathematical methods in medicine, 2015. 2015(1): p. 868493. [50] Wu, J.-D. and C.-T. Liu, Finger-vein pattern identification using principal component analysis and the neural network technique. Expert Systems with Applications, 2011. 38(5): p. 5423-5427. [51] Wu, J.-D. and C.-T. Liu, Finger-vein pattern identification using SVM and neural network technique. Expert Systems with Applications, 2011. 38(11): p. 14284-14289. [52] Yang, G., X. Xi, and Y. Yin, Finger vein recognition based on (2D) 2 PCA and metric learning. BioMed Research International, 2012. 2012(1): p. 324249. [53] Yang, Y., G. Yang, and S. Wang, Finger vein recognition based on multi-instance. International Journal of Digital Content Technology and its Applications, 2012. 6(11): p. 86-94. [54] Liu, Z., et al., Finger vein recognition with manifold learning. Journal of Network and Computer Applications, 2010. 33(3): p. 275-282. [55] Shaheed, K., et al., DS-CNN: A pre-trained Xception model based on depth-wise separable convolutional neural network for finger vein recognition. Expert Systems with Applications, 2022. 191: p. 116288. [56] Al-Nuzaili, Q., et al., Enhanced structural perceptual feature extraction model for Arabic literal amount recognition. International Journal of Intelligent Systems Technologies and Applications, 2016. 15(3): p. 240-254. [57] Hartung, D., et al. Spectral minutiae for vein pattern recognition. in 2011 international joint conference on biometrics (IJCB). 2011. IEEE. [58] Yang, J., et al. A novel finger-vein recognition method with feature combination. in 2009 16th IEEE International Conference on Image Processing (ICIP). 2009. IEEE. [59] Park, K.R., Finger vein recognition by combining global and local features based on SVM. Computing and Informatics, 2011. 30(2): p. 295-309. [60] Pflug, A., D. Hartung, and C. Busch, Feature extraction from vein images using spatial information and chain codes. Information security technical report, 2012. 17(1-2): p. 26-35. [61] Gou, J., et al., A new distance-weighted k-nearest neighbor classifier. J. Inf. Comput. Sci, 2012. 9(6): p. 1429-1436. [62] Mozumder, M.A.I., et al. Overview: Technology roadmap of the future trend of metaverse based on IoT, blockchain, AI technique, and medical domain metaverse activity. in 2022 24th International Conference on Advanced Communication Technology (ICACT). 2022. IEEE. [63] Dionisio, J.D.N., W.G.B. Iii, and R. Gilbert, 3D virtual worlds and the metaverse: Current status and future possibilities. ACM Computing Surveys (CSUR), 2013. 45(3): p. 1-38. [64] Park, S.-M. and Y.-G. Kim, A metaverse: Taxonomy, components, applications, and open challenges. IEEE access, 2022. 10: p. 4209-4251. [65] Han, Y. and S. Oh. Investigation and research on the negotiation space of mental and mental illness based on metaverse. in 2021 International Conference on Information and Communication Technology Convergence (ICTC). 2021. IEEE. [66] Lee, S.-H., Y.-E. Lee, and S.-W. Lee. Toward imagined speech based smart communication system: potential applications on metaverse conditions. in 2022 10th International Winter Conference on Brain-Computer Interface (BCI). 2022. IEEE. [67] Ying, Z., G. Li, and W. Gao, A bio-inspired multi-exposure fusion framework for low-light image enhancement. arXiv preprint arXiv:1711.00591, 2017. [68] Zhang, R. Making convolutional networks shift-invariant again. in International conference on machine learning. 2019. PMLR. [69] Deng, J., et al. Arcface: Additive angular margin loss for deep face recognition. in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019. [70] Asaari, M.S.M., S.A. Suandi, and B.A. Rosdi, Fusion of band limited phase only correlation and width centroid contour distance for finger based biometrics. Expert Systems with Applications, 2014. 41(7): p. 3367-3382. [71] Yang, W., C. Qin, and Q. Liao. A database with ROI extraction for studying fusion of finger vein and finger dorsal texture. in Biometric Recognition: 9th Chinese Conference, CCBR 2014, Shenyang, China, November 7-9, 2014. Proceedings 9. 2014. Springer. [72] Yin, Y., L. Liu, and X. Sun. SDUMLA-HMT: A multimodal biometric database. in Biometric Recognition: 6th Chinese Conference, CCBR 2011, Beijing, China, December 3-4, 2011. Proceedings 6. 2011. Springer. [73] Huang, G., et al. Densely connected convolutional networks. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. [74] Jain, A.K., P. Flynn, and A.A. Ross, Handbook of biometrics. 2007: Springer Science & Business Media. [75] Szegedy, C., et al. Going deeper with convolutions. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2015. [76] Dahl, G.E., et al., Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE Transactions on audio, speech, and language processing, 2011. 20(1): p. 30-42. [77] Ren, S., et al., Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE transactions on pattern analysis and machine intelligence, 2016. 39(6): p. 1137-1149. [78] Long, J., E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2015. [79] Mikolov, T., et al. Recurrent neural network based language model. in Interspeech. 2010. Makuhari. [80] Chen, Y.-Y., C.-H. Hsia, and P.-H. Chen, Contactless multispectral palm-vein recognition with lightweight convolutional neural network. IEEE Access, 2021. 9: p. 149796-149806. [81] Algarni, M., An Extra Security Measurement for Android Mobile Applications Using the Fingerprint Authentication Methodology. Journal of Information Security and Cybercrimes Research, 2023. 6(2): p. 139-149. [82] Qiu, X., et al., Finger vein presentation attack detection using total variation decomposition. IEEE Transactions on Information Forensics and Security, 2017. 13(2): p. 465-477. [83] Noh, K.J., et al., Finger-vein recognition based on densely connected convolutional network using score-level fusion with shape and texture images. Ieee Access, 2020. 8: p. 96748-96766. [84] Zhang, X., et al. Shufflenet: An extremely efficient convolutional neural network for mobile devices. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2018. [85] Sandler, M., et al. Mobilenetv2: Inverted residuals and linear bottlenecks. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2018. [86] Howard, A., et al. Searching for mobilenetv3. in Proceedings of the IEEE/CVF international conference on computer vision. 2019. [87] Mehta, S. and M. Rastegari, Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer. arXiv preprint arXiv:2110.02178, 2021. [88] Wu, D., et al., Skip connections matter: On the transferability of adversarial examples generated with resnets. arXiv preprint arXiv:2002.05990, 2020. [89] Zhu, X. and M. Bain, B-CNN: branch convolutional neural network for hierarchical classification. arXiv preprint arXiv:1709.09890, 2017. [90] Hu, J., L. Shen, and G. Sun. Squeeze-and-excitation networks. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2018. [91] Woo, S., et al. Cbam: Convolutional block attention module. in Proceedings of the European conference on computer vision (ECCV). 2018. [92] Zhang, Q.-L. and Y.-B. Yang. Sa-net: Shuffle attention for deep convolutional neural networks. in ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2021. IEEE. [93] Boutros, F., et al. Elasticface: Elastic margin loss for deep face recognition. in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022. [94] Lu, H., et al., A novel roi extraction method based on the characteristics of the original finger vein image. Sensors, 2021. 21(13): p. 4402. [95] He, K., et al. Deep residual learning for image recognition. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. [96] Tan, M. and Q. Le. Efficientnet: Rethinking model scaling for convolutional neural networks. in International conference on machine learning. 2019. PMLR. [97] Hsia, C.-H., L.-Y. Ke, and S.-T. Chen, Improved Lightweight Convolutional Neural Network for Finger Vein Recognition System. Bioengineering, 2023. 10(8): p. 919. [98] Ding, X., et al., Re-parameterizing your optimizers rather than architectures. arXiv preprint arXiv:2205.15242, 2022.
指導教授	王家慶(Jia-Ching Wang)	審核日期	2024-7-30
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