ITR: Fingerprint Feature Extraction and Matching

金融服务、医疗保健、电子商务、电信和政府部门的组织每天都要问数百万次与个人身份有关的问题，如“这是她声称的那个人吗？”、“这位申请人以前来过这里吗？”、“这个人是否应该访问我们的系统？”事实上，在福利支付、信用卡交易、手机通话和ATM取款方面的身份欺诈每年总计达数十亿美元。最近对国土安全的担忧使人们对个人身份识别的方法产生了额外的兴趣。个人认同是将特定个体与身份联系起来的过程。基于知识（如密码或个人识别码）和基于令牌（如身份证或钥匙）的个人自动识别方法是两种被广泛使用的传统技术。由于这些方法不是基于个人的任何固有属性，它们有明显的缺点；令牌可能丢失、被盗或放错地方，PIN可能被有效用户忘记或被冒名顶替者猜到。因此，这些方法无法区分授权人员和欺诈性获取令牌或授权人员知识的冒名顶替者。生物特征识别是指根据个体显著的生理和/或行为特征来识别个体。在各种这样的特征（如面部、声音、虹膜、指纹）中，指纹是最受欢迎的。指纹感应技术的最新进展使这些廉价的传感器可以嵌入到各种系统中（例如，笔记本电脑和pda）。在过去的40年里，指纹自动匹配系统得到了大量的应用，但其性能并不完美。提出的研究目标是：(i)可靠地提取指纹特征（细节点），以及（ii）即使在存在噪声和扭曲的指纹图像的情况下，基于这些特征开发鲁棒的指纹匹配技术。我们建议使用一些统计模型来进行特征提取和匹配。所提出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.