Sharath Pankanti

Filterbank-based fingerprint matching

With identity fraud in our society reaching unprecedented proportions and with an increasing emphasis on the emerging automatic personal identification applications, biometrics-based verification, especially fingerprint-based identification, is receiving a lot of attention. There are two major shortcomings of the traditional approaches to fingerprint representation. For a considerable fraction of population, the representations based on explicit detection of complete ridge structures in the fingerprint are difficult to extract automatically. The widely used minutiae-based representation does not utilize a significant component of the rich discriminatory information available in the fingerprints. Local ridge structures cannot be completely characterized by minutiae. Further, minutiae-based matching has difficulty in quickly matching two fingerprint images containing a different number of unregistered minutiae points. The proposed filter-based algorithm uses a bank of Gabor filters to capture both local and global details in a fingerprint as a compact fixed length FingerCode. The fingerprint matching is based on the Euclidean distance between the two corresponding FingerCodes and hence is extremely fast. We are able to achieve a verification accuracy which is only marginally inferior to the best results of minutiae-based algorithms published in the open literature. Our system performs better than a state-of-the-art minutiae-based system when the performance requirement of the application system does not demand a very low false acceptance rate. Finally, we show that the matching performance can be improved by combining the decisions of the matchers based on complementary (minutiae-based and filter-based) fingerprint information.

An identity-authentication system using fingerprints

Fingerprint verification is an important biometric technique for personal identification. We describe the design and implementation of a prototype automatic identity-authentication system that uses fingerprints to authenticate the identity of an individual. We have developed an improved minutiae-extraction algorithm that is faster and more accurate than our earlier algorithm (1995). An alignment-based minutiae-matching algorithm has been proposed. This algorithm is capable of finding the correspondences between input minutiae and the stored template without resorting to exhaustive search and has the ability to compensate adaptively for the nonlinear deformations and inexact transformations between an input and a template. To establish an objective assessment of our system, both the Michigan State University and the National Institute of Standards and Technology NIST 9 fingerprint data bases have been used to estimate the performance numbers. The experimental results reveal that our system can achieve a good performance on these data bases. We also have demonstrated that our system satisfies the response-time requirement. A complete authentication procedure, on average, takes about 1.4 seconds on a Sun ULTRA I workstation (it is expected to run as fast or faster on a 200 HMz Pentium).

Biometric cryptosystems: issues and challenges

In traditional cryptosystems, user authentication is based on possession of secret keys; the method falls apart if the keys are not kept secret (i.e., shared with non-legitimate users). Further, keys can be forgotten, lost, or stolen and, thus, cannot provide non-repudiation. Current authentication systems based on physiological and behavioral characteristics of persons (known as biometrics), such as fingerprints, inherently provide solutions to many of these problems and may replace the authentication component of traditional cryptosystems. We present various methods that monolithically bind a cryptographic key with the biometric template of a user stored in the database in such a way that the key cannot be revealed without a successful biometric authentication. We assess the performance of one of these biometric key binding/generation algorithms using the fingerprint biometric. We illustrate the challenges involved in biometric key generation primarily due to drastic acquisition variations in the representation of a biometric identifier and the imperfect nature of biometric feature extraction and matching algorithms. We elaborate on the suitability of these algorithms for digital rights management systems.

Biometric recognition: security and privacy concerns

Biometrics offers greater security and convenience than traditional methods of personal recognition. In some applications, biometrics can replace or supplement the existing technology. In others, it is the only viable approach. But how secure is biometrics? And what are the privacy implications?.

Biometric identification

W A LT ER S IP SE R For this reason, more and more organizations are looking to automated identity authentication systems to improve customer satisfaction and operating efficiency as well as to save critical resources (see Figure 1). Furthermore, as people become more connected electronically, the ability to achieve a highly accurate automatic personal identification system is substantially more critical [5]. Personal identification is the process of associating a particular individual with an identity. Identification can be in the form of verification (also known as authentication), which entails authenticating a claimed identity (“Am I who I claim I am?”), or recognition (also known as identification), which entails determining the identity of a given person from a database of persons known to the system (“Who am I?”). Knowledge-based and token-based automatic personal identification approaches have been the two traditional techniques widely used [8]. Token-based approaches use something you have to make a personal identification, such as a passport, driver’s license, ID card, credit card, or keys. Knowledge-based approaches use something you know to make a personal identification, such as a password or a personal identification number (PIN). Since these traditional approaches are not based on any inherent attributes of an individual to make a personal identification, they suffer from the

On the individuality of fingerprints

Fingerprint identification is based on two basic premises: (1) persistence and (2) individuality. We address the problem of fingerprint individuality by quantifying the amount of information available in minutiae features to establish a correspondence between two fingerprint images. We derive an expression which estimates the probability of a false correspondence between minutiae-based representations from two arbitrary fingerprints belonging to different fingers. Our results show that (1) contrary to the popular belief, fingerprint matching is not infallible and leads to some false associations, (2) while there is an overwhelming amount of discriminatory information present in the fingerprints, the strength of the evidence degrades drastically with noise in the sensed fingerprint images, (3) the performance of the state-of-the-art automatic fingerprint matchers is not even close to the theoretical limit, and (4) because automatic fingerprint verification systems based on minutia use only a part of the discriminatory information present in the fingerprints, it may be desirable to explore additional complementary representations of fingerprints for automatic matching.

Fingerprint-Based Fuzzy Vault: Implementation and Performance

Reliable information security mechanisms are required to combat the rising magnitude of identity theft in our society. While cryptography is a powerful tool to achieve information security, one of the main challenges in cryptosystems is to maintain the secrecy of the cryptographic keys. Though biometric authentication can be used to ensure that only the legitimate user has access to the secret keys, a biometric system itself is vulnerable to a number of threats. A critical issue in biometric systems is to protect the template of a user which is typically stored in a database or a smart card. The fuzzy vault construct is a biometric cryptosystem that secures both the secret key and the biometric template by binding them within a cryptographic framework. We present a fully automatic implementation of the fuzzy vault scheme based on fingerprint minutiae. Since the fuzzy vault stores only a transformed version of the template, aligning the query fingerprint with the template is a challenging task. We extract high curvature points derived from the fingerprint orientation field and use them as helper data to align the template and query minutiae. The helper data itself do not leak any information about the minutiae template, yet contain sufficient information to align the template and query fingerprints accurately. Further, we apply a minutiae matcher during decoding to account for nonlinear distortion and this leads to significant improvement in the genuine accept rate. We demonstrate the performance of the vault implementation on two different fingerprint databases. We also show that performance improvement can be achieved by using multiple fingerprint impressions during enrollment and verification.

Fuzzy vault for fingerprints

Biometrics-based user authentication has several advantages over traditional password-based systems for standalone authentication applications, such as secure cellular phone access. This is also true for new authentication architectures known as crypto-biometric systems, where cryptography and biometrics are merged to achieve high security and user convenience at the same time. In this paper, we explore the realization of a previously proposed cryptographic construct, called fuzzy vault, with the fingerprint minutiae data. This construct aims to secure critical data (e.g., secret encryption key) with the fingerprint data in a way that only the authorized user can access the secret by providing the valid fingerprint. The results show that 128-bit AES keys can be secured with fingerprint minutiae data using the proposed system.

Appearance models for occlusion handling

Abstract Objects in the world exhibit complex interactions. When captured in a video sequence, some interactions manifest themselves as occlusions. A visual tracking system must be able to track objects, which are partially or even fully occluded. In this paper we present a method of tracking objects through occlusions using appearance models. These models are used to localize objects during partial occlusions, detect complete occlusions and resolve depth ordering of objects during occlusions. This paper presents a tracking system which successfully deals with complex real world interactions, as demonstrated on the PETS 2001 dataset.

Biometrics: The future of identification

It is too early to predict where, how, and in which form reliable biometric services will eventually be delivered. But it is certain that there is no way around biometrics-based identification if we insist on positive, reliable, and irrefutable identification. As fraud in our society grows, as the pressure to deliver inexpensive authentication services mounts, and as geographically mobile individuals increasingly need to establish their identity as strangers in remote communities, the problem of reliable personal identification becomes more and more difficult. To catapult biometric technology into the mainstream identification market, it is important to encourage its evaluation in realistic contexts, to facilitate its integration into end-to-end solutions, and to foster innovation of inexpensive and user-friendly implementations. We hope that a pervasive, accountable use of biometrics technology will help establish a more open and fair society.