Christian Rathgeb

A survey on biometric cryptosystems and cancelable biometrics

Form a privacy perspective most concerns against the common use of biometrics arise from the storage and misuse of biometric data. Biometric cryptosystems and cancelable biometrics represent emerging technologies of biometric template protection addressing these concerns and improving public confidence and acceptance of biometrics. In addition, biometric cryptosystems provide mechanisms for biometric-dependent key-release. In the last years a significant amount of approaches to both technologies have been published. A comprehensive survey of biometric cryptosystems and cancelable biometrics is presented. State-of-the-art approaches are reviewed based on which an in-depth discussion and an outlook to future prospects are given.

Secure iris recognition based on local intensity variations

In this paper we propose a fast and efficient iris recognition algorithm which makes use of local intensity variations in iris textures. The presented system provides fully revocable biometric templates suppressing any loss of recognition performance.

Alignment-free cancelable iris biometric templates based on adaptive bloom filters

Biometric characteristics are largely immutable, i.e. unprotected storage of biometric data provokes serious privacy threats, e.g. identity theft, limited re-newability, or cross-matching. In accordance with the ISO/IEC 24745 standard, technologies of cancelable biometrics offer solutions to biometric information protection by obscuring biometric signal in a non-invertible manner, while biometric comparisons are still feasible in the transformed domain. In the presented work alignment-free cancelable iris biometrics based on adaptive Bloom filters are proposed. Bloom filter-based representations of binary biometric templates (iris-codes) enable an efficient alignment-invariant biometric comparison while a successive mapping of parts of a binary biometric template to a Bloom filter represents an irreversible transform. In experiments, which are carried out on the CASIA - v 3 iris database, it is demonstrated that the proposed system maintains biometric performance for diverse iris recognition algorithms, protecting biometric templates at high security levels.

Systematic Construction of Iris-Based Fuzzy Commitment Schemes

As a result of the growing interest in biometrics a new field of research has emerged entitled Biometric Cryptosystems . Only a small amount of work, which additionally tends to be custom-built according to the specific application context, has been published in this area. This work provides a systematic treatment of how to construct biometric cryptosystems based on iris biometrics. A cryptographic primitive called Fuzzy Commitment Scheme is adopted to different types of iris recognition algorithms to hide and retrieve a cryptographic key in and out of a biometric template. Experimental results confirm the soundness of the approach.

Adaptive fuzzy commitment scheme based on iris-code error analysis

Biometric cryptosystems is a group of emerging technologies that securely bind a digital key to a biometric so that no biometric image or template is stored. Focusing on iris biometrics several approaches have been proposed to bind keys to binary iris-codes where the majority of these approaches are based on the so-called fuzzy commitment scheme. In this work we present a new approach to constructing iris-based fuzzy commitment schemes. Based on intra-class error analysis iris-codes are rearranged in a way that error correction capacities are exploited more effectively. Experimental results demonstrate the worthiness of our approach.

Statistical attack against iris-biometric fuzzy commitment schemes

The fuzzy commitment scheme has been leveraged as a means of biometric template protection. Binary templates are replaced by helper data which assist the retrieval of cryptographic keys. Biometric variance is overcome by means of error correction while authentication is performed indirectly by verifying key validities. A statistical attack against the fuzzy commitment scheme is presented. Comparisons of different pairs of binary biometric feature vectors yield binomial distributions, with standard deviations bounded by the entropy of biometric templates. In case error correction consists of a series of chunks helper data becomes vulnerable to statistical attacks. Error correction codewords are bound to separate parts of a binary template among which biometric entropy is dispersed. As a consequence, chunks of the helper data are prone to statistical significant false acceptance. In experiments the proposed attack is applied to different iris-biometric fuzzy commitment schemes retrieving cryptographic keys at alarming low effort.

Two-Factor authentication or how to potentially counterfeit experimental results in biometric systems

Two-factor authentication has been introduced in order to enhance security in authentication systems. Different factors have been introduced, which are combined for means of controlling access. The increasing demand for high security applications has led to a growing interest in biometrics. As a result several two-factor authentication systems are designed to include biometric authentication. In this work the risk of result distortion during performance evaluations of two-factor authentication systems including biometrics is pointed out. Existing approaches to two-factor biometric authentication systems are analyzed. Based on iris biometrics a case study is presented, which demonstrates the trap of untruly increasing recognition rates by introducing a second authentication factor to a biometric authentication system. Consequently, several requirements for performance evaluations of two-factor biometric authentication systems are stated.

Incremental iris recognition: A single-algorithm serial fusion strategy to optimize time complexity

Daugmans algorithm, mapping iris images to binary codes and estimating similarity between codes applying the fractional Hamming Distance, forms the basis of todays commercially used iris recognition systems. However, when applied to large-scale databases, the linear matching of a single extracted iris-code against a gallery of templates is very time consuming and a bottleneck of current implementations. As an alternative to pre-screening techniques, our work is the first to present an incremental approach to iris recognition. We combine concentration of information in the first bits of an iris-code with early rejection of unlikely matches during matching stage to incrementally determine the best-matching candidate in the gallery. Our approach can transparently be applied to any iris-code based system and is able to reduce bit comparisons significantly (to about 5% of iris-code bits) while exhibiting a Rank-1 Recognition Rate being at least as high as for matches involving all bits.

Iris-Biometric Hash Generation for Biometric Database Indexing

Performing identification on large-scale biometric databases requires an exhaustive linear search. Since biometric data does not have any natural sorting order, indexing databases, in order to minimize the response time of the system, represents a great challenge. In this work we propose a biometric hash generation technique for the purpose of biometric database indexing, applied to iris biometrics. Experimental results demonstrate that the presented approach highly accelerates biometric identification.

On application of bloom filters to iris biometrics

In this study, the application of adaptive Bloom filters to binary iris biometric feature vectors, that is, iris-codes, is proposed. Bloom filters, which have been established as a powerful tool in various fields of computer science, are applied in order to transform iris-codes to a rotation-invariant feature representation. Properties of the proposed Bloom filter-based transform concurrently enable (i) biometric template protection, (ii) compression of biometric data and (iii) acceleration of biometric identification, whereas at the same time no significant degradation of biometric performance is observed. According to these fields of application, detailed investigations are presented. Experiments are conducted on the CASIA-v3 iris database for different feature extraction algorithms. Confirming the soundness of the proposed approach, the application of adaptive Bloom filters achieves rotation-invariant cancellable templates maintaining biometric performance, a compression of templates down to 20-40% of original size and a reduction of bit-comparisons to less than 5% leading to a substantial speed-up of the biometric system in identification mode.