Esta es la versión de autor del artículo publicado en: This is an author produced version of a paper published in:IEEE transactions on information forensics and security 4.4, (2009) Abstract-Automatically verifying the identity of a person by means of biometrics (e.g., face and fingerprint) is an important application in our day-to-day activities such as accessing banking services and security control in airports. To increase the system reliability, several biometric devices are often used. Such a combined system is known as a multimodal biometric system. This paper reports a benchmarking study carried out within the framework the Biosecure DS2 (Access Control) evaluation campaign organized by the University of Surrey, involving face, fingerprint and iris biometrics for person authentication, targeting the application of physical access control in a mediumsize establishment with some 500 persons. While multimodal biometrics is a well investigated subject in the literature, there exists no benchmark for a fusion algorithm comparison. Working towards this goal, we designed two sets of experiments: qualitydependent and cost-sensitive evaluation. The quality-dependent evaluation aims at assessing how well fusion algorithms can perform under changing quality of raw biometric images principally due to change of devices. The cost-sensitive evaluation, on the other hand, investigates how well a fusion algorithm can perform given restricted computation and in the presence of software and hardware failures, resulting in errors such as failure to acquire and failure to match. Since multiple capturing devices are available, a fusion algorithm should be able to handle this non-ideal but nevertheless realistic scenario. In both evaluations, each fusion algorithm is provided with scores from each biometric comparison subsystem as well as the quality measures of both the template and the query data. The response to the call of the evaluation campaign proved very encouraging, with the submission of 22 fusion systems. To the best of our knowledge, this campaign is the first attempt to benchmark quality-based multimodal fusion algorithms. In the presence of changing image quality which may be due to a change of acquisition devices and/or device capturing configurations, we observe that the top performing fusion algorithms are those that exploit automatically derived quality measurements. Our evaluation also suggests that while using all the available biometric sensors can definitely increase the fusion performance, this comes at the expense of increased cost in terms of acquisition time, computation time, the physical cost of hardware and its maintenance cost. As demonstrated in our experiments, a promising solution which minimizes the composite cost is sequential fusion, where a fusion algorithm sequentially uses match scores until a desired confidence is reached, or until all the match scores are exhausted, before outputting the final combined score. Index Terms-multimodal biometric authentication, biometric database, quality-...
Abstract. Biometric Hash algorithms, also called BioHash, are mainly designed to ensure template protection to its biometric raw data. To assure reproducibility, BioHash algorithms provide a certain level of robustness against input variability to ensure high reproduction rates by compensating for intra-class variation of the biometric raw data. This concept can be a potential vulnerability. In this paper, we want to reflect such vulnerability of a specific Biometric Hash algorithm for handwriting, which was introduced in [1], consider and discuss possible attempts to exploit these flaws. We introduce a new reconstruction approach, which exploits this vulnerability; to generate artificial raw data out of a reference BioHash. Motivated by work from Cappelli et al. for fingerprint modality in [6] further studied in [3], where such an artificially generated raw data has the property of producing false positive recognitions, although they may not necessarily be visually similar. Our new approach for handwriting is based on genetic algorithms combined with user interaction in using a design vulnerability of the BioHash with an attack corresponding to cipher-text-only attack with side information as system parameters from BioHash. To show the general validity of our concept, in first experiments we evaluate using 60 raw data sets (5 individuals overall) consisting of two different handwritten semantics (arbitrary Symbol and fixed PIN). Experimental results demonstrate that reconstructed raw data produces an EERreconstr. in the range from 30% to 75%, as compared to nonattacked inter-class EERinter-class of 5% to 10% and handwritten PIN semantic can be better reconstructed than the Symbol semantic using this new technique. The security flaws of the Biometric Hash algorithm are pointed out and possible countermeasures are proposed.
In this paper an approach for combining online signature authentication experts will be proposed. The different experts are based on one feature extraction method presented in our earlier work, the Biometric Hash algorithm [1], to which different distance measurement functions are applied. We will show that by the fusion of several algorithms with an appropriately parameterized strategy an improvement of the recognition accuracy can be achieved. The best fusion strategy results in a decrease of the EER of 12.1% in comparison to the best individual algorithm. The database we used contains 1761 genuine enrollments (with 4 signatures per enrollment), 1101 genuine verification signatures and 431 well skilled forgeries (so-called "brute force attack") by 22 persons. Based on our experimental results, we further discuss usability of alternative handwriting semantics such as pass phrases or PIN.
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