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Alternative TitleEvaluation of Biometric Recognition Algorithms
Thesis Advisor谭铁牛
Degree Grantor中国科学院研究生院
Place of Conferral中国科学院自动化研究所
Degree Discipline模式识别与智能系统
Keyword生物识别算法 测评平台 质量评测 预测模型 分层模型 多高斯分布 Biometric Algorithms Evaluation Platform Quality Evaluation Prediction Model Hierarchical Model Multi-gassian Distribution
Abstract生物识别技术发展迅速,各种算法层出不穷。生物识别产品也越来越多的被用于事关国家和公共安全的重要领域。随着其产品化进程的加速,鉴别和选择准确、稳定的识别算法的要求日益迫切。因而,构建一个高效稳定的生物识别算法评测平台以及发展科学合理的评测算法成为当前生物识别领域的一个重要课题。本文结合实验室生物测评平台建设的实践和经验,给出了详尽的测评模型理论分析,主要包括数据库构建、测试设计、数据库质量评测、错误率和置信区间计算以及预测模型分析。此外,本文还在数据库质量评测以及预测模型的研究方面做出了全新的尝试。 本文的主要工作与贡献包括: 1. 根据算法评测的任务,设计了包含数据库质量模块、比对策略模块、性能分析模块、以及预测模块的评测系统。 2. 在数据库质量模块,改进国际上认可的NIST的NFIQ指纹质量评测软件,给出大规模指纹数据库的质量标定。 3. 针对比对策略模块,提出了基于bootstrap的选择策略,以及改进的NIST比对策略和全新的用户自定义策略。 4. 在算法性能分析模块中,采用subset bootstrap方法计算错误率的置信区间,取得良好效果。 5. 在生物特征数据库的质量评测方面,提出了全新的基于分层影响因素的质量评价模型。该模型的主要特点是:根据影响因素的不同特点,即全局和局部的影响因素,分层计算数据库的质量;根据类内匹配分数的区间分布,设定质量等级;提出了子区间频率的计算方法,将匹配分数的分布与对应图像的质量相关联;该模型适用于多种模态,且各层质量均具有独立的意义。 6. 提出了全新的多高斯基的性能指标预测模型,并给出相应的实验结果和研究结论。 总体来说,本文针对目前生物识别领域全新的测评课题,成功的构建了实际测评系统,并在重要研究方向上给出了探索性的理论模型。在质量算法研究方面,提出了独特的分层模型。在预测模型方面,提出了基于高斯分布的预测模型。
Other AbstractBiometric recognition algorithms spring up in recent years for their widely applications in the guard of national and public security. The expanding market of biometric products is speeding up the development of biometric algorithms. With those miscellaneous biometric algorithms coming into being, what really matters is to find a standardized platform and efficient evaluation method to assess whether the specific biometric algorithm is efficient and robust enough for the practical applications. This thesis elaborates theoretical analysis of evaluation models combined with experiences of implementation of the biometric algorithm evaluation platform. More concretely, the following chapters will cover the construction of database, protocol design, quality evaluation and error and confidence analysis. The main contributions of our work are as follows: 1. We design and develop an evaluation platform, which contains four modules:database module, testing protocol module, performance analysis module and prediction module. 2. In the proposed database module, we improve software of NFIQ (which is developed by NIST) to evaluate quality of fingerprint databases. 3. In the testing protocol module, three kinds of strategies are proposed: the novel bootstrap based matching strategy, the improved NIST matching strategy and user-defined strategy. 4. In the analysis of algorithm performance module, we adopt the subset bootstrap based method to calculate confidence interval of error rates which is testified to achieve good results. 5. With regard to evaluation of database quality, we propose a brand new hierarchical model. This work includes the following characteristics: the quality is evaluated based on hierarchical influencing factors, local and global factors; the quality is determined quantitatively according to distribution of genuine matching scores; a subset frequency method is proposed to model relations between distribution of genuine matching scores and quality of corresponding images; the model can be extended to all modalities; quality evaluation at each level in this model can be used independently in applications. 6. We propose a performance prediction model based on Multi-Gaussian distribution assumption and present corresponding experimental results and conclusions. Generally, this thesis focus on the new issues in biometric algorithm evaluation. Firstly, we construct biometric evaluation platform successfully; secondly, we propose novel hierarchical mo...
Other Identifier200628014628031
Document Type学位论文
Recommended Citation
GB/T 7714
何倩. 生物特征识别测评方法研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2009.
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