压缩技术对人耳识别的影响研究

发布时间：2021-09-23 11:23

　　生物特征识别已广泛应用于监视应用,法医学和刑事调查。由于生物识别系统可以提供比传统的个人身份验证系统（例如令牌或密码）更高的安全性解决方案,其中令牌可能被盗,长密码或密码难以记忆并且可能被遗忘。此外,随着对法医学和诸如访问控制,移民和商业应用等安全领域的更多安全系统的需求的增加,生物识别系统最近引起了很多关注。近年来,耳印由于其显著的优势,受到了生物统计学界的广泛关注。人耳很大并且可以获得,对年龄和表情稳定,并且对于同卵双胞胎和三胞胎而言也是不同的。随着使用耳朵生物识别系统作为面部和指纹生物识别系统在许多应用中的兴趣日益增加,特别是在监视和取证上,其需要压缩耳朵图像数据,通过网络传输到特定位置。由于存储容量和传输数据的限制,可能是低质量的无线信道,在应用程序中需考虑图像压缩对其系统性能的影响。然而,以前的工作没有提出压缩技术对耳朵生物识别系统的影响。因此,本文首先研究和分析已知压缩算法（JPEG,JPEG2000和BPG）对耳朵识别系统的影响,特别是对两个公共和可用的耳朵数据库。最近,JPEG和JPEG2000标准在面部和指纹生物识别等生物识别应用中发挥了至关重要的作用。因此,这项工作... 

【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】：81 页

【学位级别】：硕士

【文章目录】：
摘要
Abstract
Chapter1.Context and Contributions
    1.1 Introduction
    1.2 Ear Biometrics
    1.3 Problem Statement and Motivation
    1.4 Thesis Objectives and Contributions
    1.5 Thesis Organization
Chapter2.Related Works for Ear Recognition and the Compression Image Techniques
    2.1 Recent Ear Recognition Approaches
        2.1.1 Geometric Feature Extraction Approaches
        2.1.2 Appearance-Based Feature Extraction Approaches
        2.1.3 Three D-based Feature Extraction Approaches
        2.1.4 Deep Feature Extraction Approaches
        2.1.5 Combining Features Approaches
        2.1.6 Matching Methods for Human Ear Recognition
    2.2 Well-Known Compression Algorithms
        2.2.1 Joint Photographic Experts Group(JPEG)
        2.2.2 Joint Photographic Experts Group2000(JPEG2000)
        2.2.3 Better Portable Graphic(BPG)
Chapter3.The Effects of JPEG and JPEG2000 on Ear Recognition System
    3.1 Introduction
    3.2 The proposed method for compression ear recognition based on JPEG and JPEG
        3.2.1 Ear representation
        3.2.2 JPEG and JPEG2000 for ear image compression
    3.3 Experimental Results
        3.3.1 The effects of JPEG on ear recognition system
        3.3.2 The effects of JPEG2000 on ear recognition system
    3.4 Summary
Chapter4.Deep Features and BPG Compression Algorithm for Ear Recognition
    4.1 Introduction
    4.2 The proposed Approach for compression ear recognition based on deep features and BPG Algorithm
        4.2.1 Better Portable Graphic(BPG)for ear image compression
        4.2.2 Deep Features Extraction
    4.3 Experimental Results
        4.3.1 Datasets and evaluation metrics
        4.3.2 Evaluation various compression techniques on ear deep features
    4.4 Comparison on the different compression techniques on ear recognition
    4.5 Summary
Conclusions
References
List of Publications
Acknowledgements
Resume



本文编号：3405615