Abstract—A wide variety of systems requires reliable personal recognition schemes to either confirm or determine the identity of an individual requesting their services. The purpose of such

Biometric systems that use a single biometric trait have to contend with noisy data, restricted degrees of freedom, failureto -enroll problems, spoof attacks, and unacceptable error rates. Multibiometric systems that use multiple traits of an individual for authentication, alleviate some of these problems while improving verification performance. We demonstrate that the performance of multibiometric systems can be further improved by learning user-specific parameters. Two types of parameters are considered here. (i) Thresholds that are used to decide if a matching score indicates a genuine user or an impostor, and (ii) weights that are used to indicate the importance of matching scores output by each biometric trait. User-specific thresholds are computed using the cumulative histogram of impostor matching scores corresponding to each user. The user-specific weights associated with each biometric are estimated by searching for that set of weights which minimizes the total verification error. The tests were conducted on a database of 50 users who provided fingerprint, face and hand geometry data, with 10 of these users providing data over a period of two months. We observed that user-specific thresholds improved system performance by 2%, while user-specific weights improved performance by 3%.

Abstract: Biometric recognition refers to an automatic recognition of individuals based on a feature vector(s) derived from their physiological and/or behavioral characteristic. Biometric recognition systems should provide a reliable personal recognition schemes to either confirm or determine the identity of an individual. Applications of such a system include computer systems security, secure electronic banking, mobile phones, credit cards, secure access to buildings, health and social services. By using biometrics a person could be identified based on "who she/he is " rather then "what she/he has " (card, token, key) or "what she/he knows" (password, PIN). In this paper, a brief overview of biometric methods, both unimodal and multimodal, and their advantages and disadvantages, will be presented.

Abstract. We introduce an authentication framework called Query-Directed Passwords (QDP) that incorporates the convenience of authentication by longterm knowledge questions and offers stronger security than from traditional types of personal questions. Security is strengthened for this scheme by imposing several restrictions on the questions and answers, and specifying how QDP is implemented in conjunction with other factors. Four QDP implementations are examined for call center applications. We examine the security and convenience of one of these implementations in detail. This implementation involves client-end storage of questions in a computer file or a wallet card, and follows a basic challenge-response authentication protocol. 1

Although the problem of automatic fingerprint matching has been extensively studied, it is nevertheless, not a fully solved problem. In this thesis, an information fusion approach is adopted to address some of the limitations of existing fingerprint matching systems. A hybrid fingerprint system that utilizes both minutiae points and ridge feature maps to represent and match fingerprint images has been developed. The hybrid matcher is shown to perform significantly better than a traditional minutiae-based matcher. The ridge feature maps extracted by this technique have also been used to align and register fingerprint image pairs via a correlation process, thereby obviating the need to rely on minutiae points for image registration. To address the problem of partial prints obtained from small-sized sensors, a fingerprint mosaicking scheme has been developed. The proposed technique constructs a composite fingerprint template from two partial fingerprint impressions by using the iterative control point (ICP) algorithm that determines the transformation parameters relating the two impressions. To mitigate the effect of non-linear distortionsin fingerprint images on the matching process, an average deformation model has

Human authentication is the security task whose job is to limit access to computer networks and physical locations only to those with authorization. This is done by equipping authorized users with passwords or tokens, or using their biometrics. However, due to human limitations, these are often used poorly, thus weakening security, or they are secure but so inconvenient as to be circumvented. This chapter describes common means for authentication as well as their strengths and weaknesses. Some of the major issues are detailed to emphasize the tradeoffs required when considering different authentication schemes. Examples of common systems applications are given with appropriate authentication choices. Finally, future trends are described to help to understand how soon and to what degree the security-convenience tradeoff will be improved.

For decades, the password has been the standard means for user authentication on computers. However, as users are required to remember more, longer, and changing passwords, it is evident that a more convenient and secure solution to user authentication is necessary. This paper examines passwords, security tokens, and biometrics – which we collectively call authenticators – and compares these authenticators and their combinations. We examine effectiveness against several attacks and suitability for particular security specifications such as compromise detection and non-repudiation. Examples of authenticator combinations and protocols are described to show tradeoffs and solutions that meet chosen, practical requirements. The paper endeavors to offer a comprehensive picture of user authentication solutions for the purposes of evaluating options for use and identifying deficiencies requiring further research.