ITR: Fingerprint Feature Extraction and Matching

与个人身份有关的问题，如“这是她声称的那个人吗？“，“这个申请人以前来过这里吗”，“这个人应该被允许进入我们的系统吗？“每天被金融服务、医疗保健、电子商务、电信和政府机构询问数百万次。事实上，在福利支付、信用卡交易、手机通话和ATM取款中的身份欺诈每年总计数十亿美元。最近对国土安全的关注引起了对个人身份识别方法的额外兴趣。个人识别是将特定个人与身份相关联的过程。基于知识（例如，密码或PIN）和基于令牌的（例如，身份证或钥匙）的自动身份识别方法一直是两个传统的技术广泛使用。由于这些方法不基于个人的任何固有属性，因此它们具有明显的缺点;令牌可能丢失、被盗或放错地方，并且PIN可能被有效用户忘记或被冒名顶替者猜测。因此，这些方法无法区分被授权的人和欺骗性地获取被授权人的令牌或知识的冒名顶替者。生物特征识别是指基于个体的独特生理和/或行为特征来识别个体。在各种这样的特性（例如，面部、声音、虹膜、指纹），指纹一直是最受欢迎的。指纹感测技术的最新进展允许这些廉价的传感器被嵌入各种系统（例如，膝上型计算机和PDA）。尽管在过去的40年里部署了大量的自动指纹匹配系统，但它们的性能并不完美。建议的研究的目标是（i）可靠地提取指纹特征（细节点），和（ii）开发强大的指纹匹配技术的基础上，这些功能，即使在存在噪声和失真的指纹图像。我们建议使用一些统计模型的特征提取和匹配。拟议的研究将导致更安全和强大的指纹匹配系统。

Questions related to the identity of an individual such as "Is this the person who she claims to be?", "Has this applicant been here before", "Should this person be given access to our system?" are asked millions of time every day by organizations in financial services, health care, e-commerce, telecommunications, and government. In fact, identity fraud in welfare disbursement, credit card transactions, cellular phone calls, and ATM withdrawals totals several billion dollars each year. Recent concerns about homeland security have created additional interest in methods for personal identification. Personal identification is the process of associating a particular individual with an identity. Knowledge-based (e.g., password or PIN) and token-based (e.g., ID card or key) automatic personal identification approaches have been the two traditional techniques widely used. Since these approaches are not based on any inherent attributes of an individual, they suffer from the obvious disadvantages; tokens may be lost, stolen, or misplaced, and a PIN may be forgotten by a valid user or guessed by an impostor. As a result, these approaches are unable to differentiate between an authorized person and an impostor who fraudulently acquires the token or knowledge of the authorized person.Biometric identification refers to identifying an individual based on his distinguishing physiological and/or behavioral characteristics. Among the various such characteristics (e.g., face, voice, iris, fingerprints), fingerprints have been the most popular. Recent advances in fingerprint sensing technology allow these inexpensive sensors to be embedded in a variety of systems (e.g., laptops and PDAs). In spite of the deployment of large number of automatic fingerprint matching systems over the past 40 years, their performance is not perfect. The objectives of the proposed research are to (i) reliably extract fingerprint features (minutiae points), and (ii) develop robust fingerprint matching techniques based on these features even in the presence of noisy and distorted fingerprint images. We propose to use a number of statistical models both for feature extraction and matching. Proposed research will lead to more secure and robust fingerprint matching systems.