Binary Biometric Representation through Pairwise Adaptive Phase Quantization

Extracting binary strings from real-valued biometric templates is a fundamental step in template compression and protection systems, such as fuzzy commitment, fuzzy extractor, secure sketch, and helper data systems. Quantization and coding is the straightforward way to extract binary representations from arbitrary real-valued biometric modalities. In this paper, we propose a pairwise adaptive phase quantization (APQ) method, together with a long-short (LS) pairing strategy, which aims to maximize the overall detection rate. Experimental results on the FVC2000 fingerprint and the FRGC face database show reasonably good verification performances.\ud \u

Pitfall of the Detection Rate Optimized Bit Allocation within template protection and a remedy

One of the requirements of a biometric template protection system is that the protected template ideally should not leak any information about the biometric sample or its derivatives. In the literature, several proposed template protection techniques are based on binary vectors. Hence, they require the extraction of a binary representation from the real- valued biometric sample. In this work we focus on the Detection Rate Optimized Bit Allocation (DROBA) quantization scheme that extracts multiple bits per feature component while maximizing the overall detection rate. The allocation strategy has to be stored as auxiliary data for reuse in the verification phase and is considered as public. This implies that the auxiliary data should not leak any information about the extracted binary representation. Experiments in our work show that the original DROBA algorithm, as known in the literature, creates auxiliary data that leaks a significant amount of information. We show how an adversary is able to exploit this information and significantly increase its success rate on obtaining a false accept. Fortunately, the information leakage can be mitigated by restricting the allocation freedom of the DROBA algorithm. We propose a method based on population statistics and empirically illustrate its effectiveness. All the experiments are based on the MCYT fingerprint database using two different texture based feature extraction algorithms

On the performance of helper data template protection schemes

The use of biometrics looks promising as it is already being applied in elec- tronic passports, ePassports, on a global scale. Because the biometric data has to be stored as a reference template on either a central or personal storage de- vice, its wide-spread use introduces new security and privacy risks such as (i) identity fraud, (ii) cross-matching, (iii) irrevocability and (iv) leaking sensitive medical information. Mitigating these risks is essential to obtain the accep- tance from the subjects of the biometric systems and therefore facilitating the successful implementation on a large-scale basis. A solution to mitigate these risks is to use template protection techniques. The required protection properties of the stored reference template according to ISO guidelines are (i) irreversibility, (ii) renewability and (iii) unlinkability. A known template protection scheme is the helper data system (HDS). The fun- damental principle of the HDS is to bind a key with the biometric sample with use of helper data and cryptography, as such that the key can be reproduced or released given another biometric sample of the same subject. The identity check is then performed in a secure way by comparing the hash of the key. Hence, the size of the key determines the amount of protection. This thesis extensively investigates the HDS system, namely (i) the the- oretical classication performance, (ii) the maximum key size, (iii) the irre- versibility and unlinkability properties, and (iv) the optimal multi-sample and multi-algorithm fusion method. The theoretical classication performance of the biometric system is deter- mined by assuming that the features extracted from the biometric sample are Gaussian distributed. With this assumption we investigate the in uence of the bit extraction scheme on the classication performance. With use of the the- oretical framework, the maximum size of the key is determined by assuming the error-correcting code to operate on Shannon's bound. We also show three vulnerabilities of HDS that aect the irreversibility and unlinkability property and propose solutions. Finally, we study the optimal level of applying multi- sample and multi-algorithm fusion with the HDS at either feature-, score-, or decision-level

Hand-based multimodal identification system with secure biometric template storage

WOS:000304107200001This study proposes a biometric system for personal identification based on three biometric characteristics from the hand, namely: the palmprint, finger surfaces and hand geometry. A protection scheme is applied to the biometric template data to guarantee its revocability, security and diversity among different biometric systems. An error-correcting code (ECC), a cryptographic hash function (CHF) and a binarisation module are the core of the template protection scheme. Since the ECC and CHF operate on binary data, an additional feature binarisation step is required. This study proposes: (i) a novel identification architecture that uses hand geometry as a soft biometric to accelerate the identification process and ensure the system's scalability; and (ii) a new feature binarisation technique that guarantees that the Hamming distance between transformed binary features is proportional to the difference between their real values. The proposed system achieves promising recognition and speed performances on two publicly available hand image databases.info:eu-repo/semantics/acceptedVersio

Fingerprint Verification Using Spectral Minutiae Representations

Most fingerprint recognition systems are based on the use of a minutiae set, which is an unordered collection of minutiae locations and orientations suffering from various deformations such as translation, rotation, and scaling. The spectral minutiae representation introduced in this paper is a novel method to represent a minutiae set as a fixed-length feature vector, which is invariant to translation, and in which rotation and scaling become translations, so that they can be easily compensated for. These characteristics enable the combination of fingerprint recognition systems with template protection schemes that require a fixed-length feature vector. This paper introduces the concept of algorithms for two representation methods: the location-based spectral minutiae representation and the orientation-based spectral minutiae representation. Both algorithms are evaluated using two correlation-based spectral minutiae matching algorithms. We present the performance of our algorithms on three fingerprint databases. We also show how the performance can be improved by using a fusion scheme and singular points

Proceedings of the 35th WIC Symposium on Information Theory in the Benelux and the 4th joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux, Eindhoven, the Netherlands May 12-13, 2014

Compressive sensing (CS) as an approach for data acquisition has recently received much attention. In CS, the signal recovery problem from the observed data requires the solution of a sparse vector from an underdetermined system of equations. The underlying sparse signal recovery problem is quite general with many applications and is the focus of this talk. The main emphasis will be on Bayesian approaches for sparse signal recovery. We will examine sparse priors such as the super-Gaussian and student-t priors and appropriate MAP estimation methods. In particular, re-weighted l2 and re-weighted l1 methods developed to solve the optimization problem will be discussed. The talk will also examine a hierarchical Bayesian framework and then study in detail an empirical Bayesian method, the Sparse Bayesian Learning (SBL) method. If time permits, we will also discuss Bayesian methods for sparse recovery problems with structure; Intra-vector correlation in the context of the block sparse model and inter-vector correlation in the context of the multiple measurement vector problem