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Biometrics for User Authentication - Biometrics in Context of Security Goals, Fusion Strategies for Biometrics, Knowledge and Possession

example information recognition fingerprints

Claus Villager and Jana Dittmann
Otto-von-Gerick University
Magdeburg, Germany

Definition: User authentication can be viewed at as the problem of binding identities to subjects. Authentication can be based on biometrics information, which uses physiological or behavioral traits.

Biometrics in Context of Security Goals

Recently security has become one of the most significant and challenging problems for spreading new information technology. As summarized in the following security requirements are essential for multimedia systems: confidentiality, data integrity, data origin authenticity, entity authenticity, non-repudiation and availability. The goal of entity authenticity is to ensure that an entity is the one it claims to be and is useful, for example, to implement further access restrictions or monitor the usage of a service. Entities taking part in a communication can be proven by user authentication protocols. In the following we introduce to a general information flow model for a generic user authentication system, describe the overall components of such a system and give two examples for the information flow protocol based on password and biometrics. In some applications, user authentication require a declaration of intention and therefore we discuss the overall impact of physiological and behavioral biometrics on that matter, stress the psychological impact of identification and observe ascertainability problems with respect to the overall environmental conditions. To enhance the security of biometric user authentication systems, fusion strategies for biometrics, knowledge and possessions play an important role for future applications. On the example of two protocols, multi-factor authentication approaches are summarized: the fusion of passphrase recognition and handwriting biometrics and an approach to combining knowledge and fingerprints.

Fusion Strategies for Biometrics, Knowledge and Possession

The combination of information from at least two of the categories biometrics, knowledge and possession, sometimes also called multi-factor authentication, can be advantageous mainly due to two reasons: Firstly, it can achieve higher recognition accuracy, i.e. a tendency towards lower error rates. Secondly, it may compensate for those cases, where one or more types of authentication information A is unavailable at time or place of the authentication attempt. Looking at large distributed systems with a great number of workstations, for example, it is quite possible that these workstations have numerous different devices for user authentication. Some of them might be equipped with smart card readers, other with (different types) of biometric sensors and some simply with a keyboard for textual input. Further, combinations of these types of devices can be considered.

In this scenario, multi-factor fusion may allow to ensure a specific security level for those cases, where users move between workstations. If for example, in a scenario where some computers are equipped with an iris recognition system, which is believed to be extremely accurate , but others only with keyboards and smart card readers, one exemplary access control strategy could be formulated as follows. On those workstations equipped with an iris scanner, (unimodal) biometric authentication is sufficient to grant access by users. For the remaining computers, a dual-factor authentication is required, based on possession (smart card) and knowledge (password). Obviously, such an approach of multi-factor authentication can be arbitrarily extended by any other combination of biometric authentication methods. Figure 3 illustrates the fusion concept and the resulting four constellations (Knowledge & Biometrics, Knowledge and Possession, Biometrics and Possession and all three factors) are shown as the respective intersecting planes in the figure.

In this subsection, two selected approaches for combining knowledge and biometrics shall be introduced. One approach focuses on the biometric modality of handwriting, whereas the second scheme targets at using fingerprints recognition.

Passphrase-based User Authentication based on Handwriting

Modern digitizer tablets such as those integrated in Tablet PCs or PDAs open many new possibilities for recognition tasks. Two main disciplines in this area are user authentication based on dynamic features of writing process (e.g. for signatures) and the recognition of textual content of handwriting. The first category, also denoted as online signature verification, has been researched over the past two decades. A wide variety of methods can be found today, which attempt to utilize dynamic characteristics such as pen tip acceleration velocity or pressure applied to the writing surface to recognize writers by their writing behavior Similarly to online signature verification, online handwriting recognition utilizes dynamic features of the writing process as well and has also been a research topic over the past two decades. Just like in biometrics, error rates are intrinsic to handwriting recognition and can be described for example in terms of correctly recognized letters from an original’s ground truth.

The complementary goals of these two categories of handwriting applications allow for the design of a fusion strategy, whereby subjects use passwords or passphrases as authentication information A . For the part of the authentication system U , the complementary (reference) information C=C biometric | | C groundtruth is composed of a concatenation (denoted by | |) of the biometric features C biometric and the ground truth (i.e. textual content) C groundtruth of the passphrase.

Authentication is performed by two independent experts: one biometric expert determines a similarity between A and C with respect to biometric features, as known for example from signature verification. The second expert performs a textual recognition to A and compares the resulting text to the ground truth, which is stored as part of C . In the first case, the result can be a biometric matching score, denoting the degree of similarity between the two information. In the second case, since recognition errors have to be considered, string edit distances, for example the Levensthein distance can be used in order to find a measure of string similarity. Finally, based on these two similarity measures, various fusion strategies, as known from multimodal biometrics, can be applied to implement an authentication decision. This can be achieved for example by weighted addition of matching scores and comparison to a given decision threshold.

Combining Knowledge and Fingerprints

The combination of knowledge and fingerprint biometrics has been an area of research followed by science and industry. One consequence of this is that a number of approaches have been registered as patents for example. In a general description, the fusion of knowledge and fingerprint biometrics can be achieved in the following way: Assuming that for each of the i [ 1 n Users ] users H i of an authentication system U , features of images of k i different fingers are registered with the system, where k i > 1 . Assuming that:

i) at least one feature description of each of the k , fingers exists and

ii) additionally at least one sequence R i,n ( n >= 1 ) exists for authentication of user H i , whereby a set of m i,n fingerprint assignments exists ( m i,n <= k i ) referencing the registered k i fingerprint features,

then, an authentication function l can be designed in a way, that only fingerprints provided in a certain, user specific sequence to the biometric sensor, leads to a successful authentication. With this simple method, for example biometric passwords or personal identification numbers (PIN) can be implemented, as explained by the following example.

Assuming each of the users has registered all k i =10 fingerprints with U , she or he can virtually associate a digit number scheme with the fingers. For example increasing assignment of digits from left to right: 0 for the little finger of the left hand, 1 for the ring finger and so forth, reaching 9 for the little finger of the right hand. Such a virtual assignment is illustrated in Figure 4.

Given such an assignment of fingers to digits, each user is enabled to reproduce a PIN by sequences of fingerprints and consequently, combine knowledge and biometrics for authentication. Naturally with this scheme, alternative assignments of fingers to codes can be found, for example alphabet letters allowing implementation of biometric passwords. Furthermore, the coding suggested here allows to utilize challenge-response protocols, whereby the computer challenges specific sequences of code words as authentication information A , which can only be produced by individuals having both knowledge about the individual coding as well as the biometric characteristics.

Apparently, with strategies for combining knowledge and biometrics as described in this subsection, user authentication can be designed in such way that:

  • Authentication information A becomes substitutable. Even if one or more fingerprints do get compromised, by changing for example the virtual key, fingerprints may still be used for recognition of users.
  • Context-specific authentication information A : users may choose different fingerprint sequences for different applications and contexts.
  • Increase in security level: by logical conjunction of each individual fingerprint verification process, it is possible to reduce the False-Acceptance rates drastically, as we can expect that error probabilities multiply, assuming statistical independence of each of the verification processes. However, future experimental evaluations will have to demonstrate how this will affect the other error classes such as False Rejection Rates in practice.
Biometrics Security - INTRODUCTION, Biometric Security, BACKGROUND, MAIN FOCUS, Privacy, Awareness and Consent, Risks, SPOOFING BIOMETRIC SECURITY, CONCLUSION [next] [back] Biometrics, A Critical Consideration in Information Security Management - INTRODUCTION, BACKGROUND, BIOMETRICS TECHNOLOGIES, Retina and Iris Scanning, Fingerprint Scanning, Facial Recognition, Voice Recognition, PRACTITIONER IMPLICATIONS

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