Statistical Methods for Fingerprint Image Analysis

指纹图像分析的统计方法

• 批准号: 0706385
• 负责人: Sarat Dass
• 金额: $ 20万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0706385&HistoricalAwards=false
• 关键词: Statistical; Methods; Fingerprint; Image; Analysis

Biometric recognition, or biometrics, refers to the automatic recognition of a person based on his anatomical or behavioral characteristics. Among the various biometric traits (e.g., face, iris, fingerprint, voice), fingerprint-based authentication has the longest history, and it has been successfully adopted in both forensic and civilian applications. However, the performance of current fingerprint recognition systems is inadequate in the presence of noisy and deformed images, especially when the fingerprint database is very large. Three areas of statistical research are impacted, namely, the analysis of functional data, multivariate dependence, and spatial and general point processes. The proposed research will develop and utilize a Bayesian framework and related computational schemes for inference. This framework will be used to address following specific problems: fingerprint feature detection, modeling noisy and deformed images, fingerprint individuality (i.e., uniqueness) assessment, and effective distributional representations for information fusion (multi-biometrics).Advances in fingerprint capture technology have resulted in new large scale civilian applications such as the US-VISIT program. However, these systems still encounter difficulties due to the effects of biometric variability present in operating environments and the massive number of comparisons that have to be executed in each identification task. The proposed model-based methods are likely to improve the effectiveness of various fingerprint processing tasks which will eventually yield improved identification performance in real operating environments. Further, this research will impact how fingerprint evidence is reported and used for the identification of suspects. The proposed research increases the role of statistics in important computer science and engineering applications, and provides an impetus for inter-disciplinary research and synergistic activities. Both undergraduate and graduate students working on the proposed topics will develop the analytical and computing skills required to perform scientific research. In this way, the proposed research helps in the creation of future scientists to work in the emerging and critical field of biometric recognition.

生物特征识别，或称为生物特征识别，是指根据一个人的解剖或行为特征自动识别他。在各种生物特征（例如面部、虹膜、指纹、声音）中，基于指纹的身份验证历史最悠久，并且已成功应用于法医和民用应用。然而，当前的指纹识别系统在存在噪声和变形图像的情况下性能不足，特别是当指纹数据库非常大时。统计研究的三个领域受到影响，即功能数据分析、多元依赖性以及空间和一般点过程。拟议的研究将开发和利用贝叶斯框架和相关计算方案进行推理。该框架将用于解决以下具体问题：指纹特征检测、噪声和变形图像建模、指纹个性（即唯一性）评估以及信息融合的有效分布表示（多生物识别技术）。指纹捕获技术的进步带来了新的大规模民用应用，例如 US-VISIT 计划。然而，由于操作环境中存在的生物特征变异的影响以及每个识别任务中必须执行的大量比较，这些系统仍然遇到困难。所提出的基于模型的方法可能会提高各种指纹处理任务的有效性，最终将在实际操作环境中提高识别性能。此外，这项研究还将影响指纹证据的报告和用于识别嫌疑人的方式。拟议的研究增强了统计学在重要的计算机科学和工程应用中的作用，并为跨学科研究和协同活动提供了动力。研究拟议主题的本科生和研究生都将培养进行科学研究所需的分析和计算技能。通过这种方式，所提出的研究有助于培养未来的科学家在生物识别这一新兴的关键领域工作。