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自动指纹识别算法的识别性能研究
其他题名Research on Performance of Automation Fingerprint Verification Algorithm
任群
学位类型工学博士
导师田捷
2003-05-01
学位授予单位中国科学院研究生院
学位授予地点中国科学院自动化研究所
学位专业模式识别与智能系统
关键词模式识别 指纹特征 统计分析 类bootstrap估计 错误率 性能评估 Pattern Recognition Fingerprint Features Statistical Analysis Subset Bootstrap Method Error Rate Performace Evaluation
摘要自动指纹识别是一个挑战性很强的交叉学科,它涉及图像处理、模式识别、 计算机等多种研究领域。迄今为止,该技术的研究已经取得了巨大的成就,也面 临着一些尚待解决的困难问题。如何评估识别算法的识别性能并分析特征对识别 算法误差的影响是这个研究领域的重大挑战,也是近年国际模式识别会议、学术 杂志和相关竞赛的研究热点。各种识别算法都具有自身的特点和优势。充分挖掘 诸方法的特色,实现优势互补,是自动指纹识别技术的重要发展方向之一。 本文主要采用统计分析的方法对自动指纹识别算法的性能进行了深入的研 究。根据识别错误产生的原因的不同,将算法识别错误率分为经验错误率和固有 错误率两种。分别对算法经验错误率的评估方法和固有错误率的理论计算等研究 问题进行了一些细致的算法探讨和实验分析。取得的研究成果和创新之处概括如 下: (1)归纳总结出一种自动指纹识别技术的研究框架,将所有指纹识别的研究 问题根据研究对象的不同,归纳为特征层、算法层、评估层和应用层四个层次。 较全面地归纳了指纹的各种特征定义,并分析了近几年来指纹特征提取算法和指 纹匹配算法层的研究进展。对指纹识别算法的研究具有一定的参考价值和启示意 义。 (2)提出以算法相等错误率的置信区间作为衡量算法稳定性的一个评估指 标。提出了一种类Bootstrap统计分析方法用于估计算法稳定性。理论分析和实 验证明,该方法优越于计算置信区间的传统的参数或非参数方法,可以在分布未 知且数据量较少的情况下,更精确地计算拒识率、误识率等算法准确性评价指标 的置信区间,进而估计出算法识别性能的最好和最差的范围。这种方法可以推广 到其他几种生物特征识别技术中。 (3)提出一种基于杆特征的概率模型,用来估计所有可能的指纹图像中,和 给定指纹的足够相似的概率。可以粗略估计基于杆特征匹配的指纹识别算法的固 有错误率上界。FVC2002数据库上的实验表明,指纹细节点的杆特征具有很好 的独特性,可以代替六位密码满足身份认证的要求。 (4)提出一种改进的模型,用来估计给定两个来自不同手指的指纹之间的相 似的概率。采用杆特征充分表达了细节点之间的关系,弥补了传统方法假设细节 点相互独立且均匀分布的不足。统计结果更接近于实际情况。 (5)实现用x2方法,Kolmogorov-Smimov方法等多种统计方法研究匹配分 数的分布规律,验证了指纹总体匹配分数不满足独立同分布,仅占总体少数
其他摘要Automatic fingerprint identification is a challenging interdisciplinary field, which includes image processing, pattern recognition, computer technology and so on. Currently, research on fingerprint recognition has received considerable achievements, but some critical problems in this field are still needed to be resolved. The performance evaluation of automatic fingerprint identification algorithm is one of the important issues in international conferences and journals on pattern recognition. In this thesis, we have identified and explored the performance of automatic fingerprint identification algorithm based on statistical analysis. The calculation of empirical error rates and theoretical estimation of intrinsic error rates are investigated, respectively. The contribution of this dissertation is as follows: (1) We have developed a research framework to provide some insights into the strengths and limitations of the automation in matching fingerprints. An introduction to state-of-the-art in fingerprint identification technology as well as previous research done in this field is presented. (2) A subset bootstrap algorithm to measure confidence interval of equal error rate, so that the stability performance of different automatic fingerprint identification algorithms on the same data sets can be compared. We systematically study and compare this subset bootstrap technique with the conventional parametric and nonparametric (bootstrap) methods for measuring confidence intervals. Experimental results show that the subset bootstrap method gives accurate indication of the significance of the estimates such as FMR, FNMR, and EER. (3) A statistical model based on probability is proposed to estimate the uniqueness of a fingerprint template. The model is used to assess the performance limitations of popular fingerprint verification algorithm based on the pair-mate minutiae representation of fingerprints. Results are shown using FVC2002 database. These results contribute towards making fingerprint matching a science and setting the legal challenges to fingerprints. (4) We theoretically estimate the probability of a false correspondence between two fingerprints from different fingers using the pair-mate minutiae representation ot fingerprints. Unlike the previous work, which assumes that the minutiae are independent distributed, in our model, we don not make this assumption and measure the positions and orientations of pair-mate minutiae. The rates obtained by our approach are significantly lower than that of previously published research. (5) The distribution of matching scores is analysised using some statistics such as the chi-square measure, Kolmogorov-Smirnov measure, and so on. Experimental results show that the match scores are not independently identically distributed for all subjects, and suggest that a significant part of the error is due to few fingers whose data are not i.i.d.
馆藏号XWLW747
其他标识符747
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/5756
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
任群. 自动指纹识别算法的识别性能研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2003.
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