False-Accept/False-Reject Trade-Offs for Ensembles of Biometric Authentication Systems

Abstract

Biometric authentication systems, based on secret key generation, work as follows. In the enrollment stage, an individual provides a biometric signal that is mapped into a secret key and a helper message, the former being prepared to become available to the system at a later time (for authentication), and the latter is stored in a public database. When an authorized user requests authentication, claiming his/her identity to be one of those of the subscribers, he/she has to provide a biometric signal again, and then the system, which retrieves also the helper message of the claimed subscriber, produces an estimate of the secret key, that is finally compared to the secret key of the claimed user. In case of a match, the authentication request is approved, otherwise, it is rejected.Evidently, there is an inherent tension between two desired, but conflicting, properties of the helper message encoder: on the one hand, the encoding should be informative enough concerning the identity of the real subscriber, in order to approve him/her in the authentication stage, but on the other hand, it should not be too informative, as otherwise, unauthorized imposters could easily fool the system and gain access. A good encoder should then trade off the two kinds of errors: the false reject (FR) error and the false accept (FA) error.In this work, we investigate trade-offs between the random coding FR error exponent and the best achievable FA error exponent. We compare two types of ensembles of codes: fixed-rate codes and variable-rate codes, and we show that the latter class of codes offers considerable improvement compared to the former. In doing this, we characterize optimal rate functions for both types of codes. We also examine the effect of privacy leakage constraints for both fixed-rate codes and variable-rate codes.Index Terms: biometric systems, secret sharing, error exponents, random binning, fixed-length, variable-length, privacy leakage.We consider a biometric authentication system which is based on the one described in [6, Sections 2.2-2.6], and on the notion of secret key generation and sharing of Maurer [7] and Ahlswede and Csiszár [1], [2]. In particular, this system works in the following manner. In the enrollment phase, a person that subscribes to the system, feeds it with a biometric signal, X = (X 1 , X 2 , . . . , X n ).The system then responds by generating (using its encoder) two outputs. The first is a secret key, S, at rate R s and the second is a helper message, W , at rate R w . The secret key will be used by the system later, at the authentication stage and the helper message is saved in a database. When an authorized user (a subscriber) wishes to sign in, claiming to be one of the subscribers that have already enrolled, he/she is requested to provide again his/her biometric signal, Y = (Y 1 , . . . , Y n ) (correlated to X, if indeed from the same person, or independent, otherwise). The system then retrieves the helper message W of the claimed subscriber from the database, and responds (using its decoder...

NeuroBiometric: An eye blink based biometric authentication system using an event-based neuromorphic vision sensor

“…Existing explicit and static biometric authentication features could be less difficult to mimick and may pose a threat to people's privacy and security. To pursue more secure systems, researchers proposed several implicit biometrics identification approaches [12], [13]. Among all the bio-signals, eye movement signals are relatively simpler to process in terms of data acquisition, and usually utilizes EOG and videooculography (VOG) to study biometric authentication systems.…”