基于心电图和指纹的多生物识别方法

发布时间：2022-01-24 15:49

　　传统的身份验证策略（如密码和智能卡）因为它们可以被共享、遗忘、复制、操纵或伪造,其安全性存在隐患。与传统方法不同,生物识别是基于人的生理或行为特征进行身份识别的科学,已成为确定个体身份的合法方法。如今,生物识别技术已不再局限于刑事执法,更多企业使用生物识别技术来管理对建筑物和信息的访问。然而,大多数单模态生物特征识别受到诸如噪声数据,非普遍性和欺骗攻击之类的限制,使得它无法达到现实世界应用的性能要求。为了克服单模态方法的这些缺点,本文提出了一种新颖的安全多模态生物识别方法,使用不同的融合方法将心电图（ECG）与指纹相结合。该方法克服了单一方法的局限性,提高了整体方法的性能并增强了安全性,对欺骗攻击具有更好的鲁棒性。与其他多模态生物识别方法（例如,面部,耳朵和基于指纹的多模态生物识别系统）相比,ECG信号可以容易地从手指获取,这使得系统非常方便和有效。此外,ECG信号只能从活人身体获取,因此还可以据此进行活体检测,使系统具有更强的抗攻击能力。本文的第一部分研究了心电图和指纹作为单模态生物识别的方法。为此,我们首先提出一种改进的生物哈希和矩阵运算方法,为生物识别系统生成了一种新的可取消的心... 

【文章来源】：哈尔滨工业大学黑龙江省 211工程院校 985工程院校

【文章页数】：143 页

【学位级别】：博士

【文章目录】：
摘要
Abstract
Abbreviations
Chapter1 Introduction
    1.1 Motivation for Multi-biometrics
        1.1.1 Multimodal biometric systems
        1.1.2 Fusion in Multimodal Biometrics
    1.2 Motivation for Applying ECG and Fingerprint Multi-Biometrics
    1.3 Research Goals and Contributions
    1.4 Outline of Thesis
Chapter2 Related Work and Existing Databases
    2.1 Recent ECG Biometric Methods
        2.1.1 Conventional Machine Learning Approaches
        2.1.2 Deep Learning-Based Approaches
    2.2 Recent Fingerprint Biometric Methods
        2.2.1 Conventional Machine Learning Approaches
        2.2.2 Deep Learning-Based Approaches
    2.3 Recent Multimodal Biometric Methods based on ECG and Fingerprints.
    2.4 Existing ECG Databases
    2.5 Existing Fingerprint Databases
    2.6 The Multimodal ECG/Fingerprint Databases
    2.7 Summary
Chapter3 ECG and Fingerprint Biometric Authentication Method
    3.1 Introduction
    3.2 Proposed Cancelable Biometric Method based on ECG
        3.2.1 ECG Feature Extraction Algorithm
        3.2.2 ECG Feature Template Protection Methods
            3.2.2.1 Cancelable ECG using improved Bio-Hashing method
            3.2.2.2 Build cancelable ECG using an input feature matrix
        3.2.3 Feed Forward Neural Network(FFNN)
    3.3 Fingerprint Classification Algorithm
        3.3.1 Fingerprint Preprocessing and Feature Extraction
        3.3.2 QG-MSVM Classifier
    3.4 Experimental Results
        3.4.1 Performance of the Proposed Cancelable Method
            3.4.1.1 Evaluation of improved Bio-Hash algorithm requirement
            3.4.1.2 Evaluation of Matrix operation algorithm requirement
            3.4.1.3 Results of the proposed cancelable method
        3.4.2 Performance of Fingerprint Classification Algorithm
            3.4.2.1 Results of the proposed classifier
            3.4.2.2 Discussion
    3.5 Summary
Chapter4 Multimodal Biometric Authentication Methods Using Convolution Neural Network based on Different Level Fusion of ECG and Fingerprint
    4.1 Introduction
    4.2 The Proposed Hierarchical Multimodal Method using CNN based on Decision Level Fusion
        4.2.1 First Stage of Hierarchical Multimodal Method(ECG Authentication)
        4.2.2 Second Stage of Hierarchical Multimodal Method(Fingerprint Authentication)
        4.2.3 Extracting the Features of ECG and Fingerprint(The Proposed CNN model)
        4.2.4 Updating of ECG and Fingerprint Templates(Cancelable Method)
        4.2.5 Classification of ECG and Fingerprint
        4.2.6 Internal Fusion of ECG and Fingerprint
        4.2.7 Data Augmentation
        4.2.8 Decision Fusion of ECG and Fingerprint
    4.3 The Proposed Parallel Multimodal Method using CNN based on Feature Level Fusion
    4.4 Evaluation and Results
        4.4.1 Multimodal Datasets
        4.4.2 Experimental Setup
        4.4.3 Fusion of ECG and Fingerprint(the first method)
        4.4.4 Fusion of ECG and Fingerprint(the second method)
    4.5 Discussion
        4.5.1 Effect of Dataset Augmentation
        4.5.2 Effect of Template Protection Method
        4.5.3 Computational Costs
    4.6 Summary
Chapter5 Parallel Score Fusion of ECG and Fingerprint for Human Authentication based on Convolution Neural Network
    5.1 Introduction
    5.2 The Proposed Multimodal Method using CNN based on Parallel Score Fusion
        5.2.1 First Step of Multimodal Method(ECG Authentication)
        5.2.2 Second Step of Multimodal Method(Fingerprint Authentication)
        5.2.3 ECG and Fingerprint Templates protection(Matrix Operation method) ..
        5.2.4 Classification of ECG and Fingerprint
        5.2.5 Parallel Score Fusion
        5.2.6 Data Augmentation
    5.3 Experimental Setup and Results
        5.3.1 Multimodal Dataset
        5.3.2 Experimental Setup
        5.3.3 Fusion of ECG and fingerprint
        5.3.4 Discussion
    5.4 Summary
Conclusions
References
List of Publications
Acknowledgements
Resume


【参考文献】：
期刊论文
[1]Fingerprint Liveness Detection Based on Multi-Scale LPQ and PCA[J]. Chengsheng Yuan,Xingming Sun,Rui Lv.  中国通信. 2016(07)



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