Marta Gomez-Barrero

Iris image reconstruction from binary templates: An efficient probabilistic approach based on genetic algorithms

A binary iriscode is a very compact representation of an iris image. For a long time it was assumed that the iriscode did not contain enough information to allow for the reconstruction of the original iris. The present work proposes a novel probabilistic approach based on genetic algorithms to reconstruct iris images from binary templates and analyzes the similarity between the reconstructed synthetic iris image and the original one. The performance of the reconstruction technique is assessed by empirically estimating the probability of successfully matching the synthesized iris image against its true counterpart using a commercial matcher. The experimental results indicate that the reconstructed images look reasonably realistic. While a human expert may not be easily deceived by them, they can successfully deceive a commercial matcher. Furthermore, since the proposed methodology is able to synthesize multiple iris images from a single iriscode, it has other potential applications including privacy enhancement of iris-based systems.

On-line signature recognition through the combination of real dynamic data and synthetically generated static data

On-line signature verification still remains a challenging task within biometrics. Due to their behavioural nature (opposed to anatomic biometric traits), signatures present a notable variability even between successive realizations. This leads to higher error rates than other largely used modalities such as iris or fingerprints and is one of the main reasons for the relatively slow deployment of this technology. As a step towards the improvement of signature recognition accuracy, the present paper explores and evaluates a novel approach that takes advantage of the performance boost that can be reached through the fusion of on-line and off-line signatures. In order to exploit the complementarity of the two modalities, we propose a method for the generation of enhanced synthetic static samples from on-line data. Such synthetic off-line signatures are used on a new on-line signature recognition architecture based on the combination of both types of data: real on-line samples and artificial off-line signatures synthesized from the real data. The new on-line recognition approach is evaluated on a public benchmark containing both real versions (on-line and off-line) of the exactly same signatures. Different findings and conclusions are drawn regarding the discriminative power of on-line and off-line signatures and of their potential combination both in the random and skilled impostors scenarios. HighlightsNovel generation method of dynamically enhanced synthetic off-line signatures.Novel on-line signature verification architecture.Findings on the fusion of on-line and off-line signature.Findings on the differences between the random and skilled forgeries scenarios.Fully reproducible protocol carried out on a large public benchmark.

Efficient software attack to multimodal biometric systems and its application to face and iris fusion

In certain applications based on multimodal interaction it may be crucial to determine not only what the user is doing (commands), but who is doing it, in order to prevent fraudulent use of the system. The biometric technology, and particularly the multimodal biometric systems, represent a highly efficient automatic recognition solution for this type of applications. Although multimodal biometric systems have been traditionally regarded as more secure than unimodal systems, their vulnerabilities to spoofing attacks have been recently shown. New fusion techniques have been proposed and their performance thoroughly analysed in an attempt to increase the robustness of multimodal systems to these spoofing attacks. However, the vulnerabilities of multimodal approaches to software-based attacks still remain unexplored. In this work we present the first software attack against multimodal biometric systems. Its performance is tested against a multimodal system based on face and iris, showing the vulnerabilities of the system to this new type of threat. Score quantization is afterwards studied as a possible countermeasure, managing to cancel the effects of the proposed attacking methodology under certain scenarios.

Protected Facial Biometric Templates Based on Local Gabor Patterns and Adaptive Bloom Filters

Biometric data are considered sensitive personal data and any privacy leakage poses severe security risks. Biometric templates should hence be protected, obscuring the biometric signal in a non-reversible manner, while preserving the unprotected systems performance. In the present work, irreversible face templates based on adaptive Bloom filters are proposed. Experiments are carried out on the publicly available Bio Secure DB utilizing the free Bob image processing toolbox, so that research is fully reproducible. The performance and security evaluations proof the irreversibility of the protected templates, while preserving the verification performance. Furthermore, template size is considerably reduced.

Unlinkable and irreversible biometric template protection based on bloom filters

New framework for the analysis of the unlinkability of protected templates.New unlinkable and irreversible biometric template protection scheme.Biometric Template Protection scheme robust to cross-matching attacks.Thorough eval of compliance with ISO/IEC IS 24745- biometric inf protection. Deployments of biometric technologies are already widely disseminated in numerous large-scale nation-wide projects. Since the protection of biometric reference data is of particular concern in order to safeguard individuals privacy, biometric template protection schemes are designed to handle biometric reference data in an irreversible and unlinkable manner. In past years, schemes based on Bloom filters have been introduced and applied to various characteristics. However, thorough security analyses have exposed the original concept to be vulnerable to cross-matching attacks.In this article we present a general framework for the evaluation of unlinkability in biometric template protection schemes, as well as an improved, unlinkable and irreversible, system based on Bloom filters. In order to generate cross-matching resistant protected templates we re-design the original scheme and propose an additional, easily integrable, processing step, which is referred to as structure-preserving feature re-arrangement. The improved system is thoroughly evaluated on the publicly available face corpus of the BioSecure Multimodal Database. It is shown that the proposed scheme maintains the biometric performance of the unprotected system. Moreover, cross-matching resistance is achieved in the presence of existing attacks, considering adversary models where potential attackers are in possession of protected biometric templates as well as secret credentials.

Multi-biometric template protection based on Homomorphic Encryption

New framework for multi-biometric template protection based on Homomorphic Encryption.Thorough eval of compliance with ISO/IEC IS 24745 on biometric information protection.Detailed complexity overhead analysis. In spite of the advantages of biometrics as an identity verification technology, some concerns have been raised due to the high sensitivity of biometric data: any information leakage poses a severe privacy threat. To solve those issues only protected templates should be stored or exchanged for recognition purposes. In order to improve the performance and achieve more secure and privacy-preserving systems, we propose a general framework for multi-biometric template protection based on homomorphic probabilistic encryption, where only encrypted data is handled. Three fusion levels are thoroughly analysed, showing that all requirements described in the ISO/IEC 24745 standard on biometric data protection are met with no accuracy degradation. Furthermore, even if all the process is carried out in the encrypted domain, no encryptions are necessary during verification, thereby allowing an efficient verification which can be deployed for real-time applications. Finally, experiments are carried out on a reproducible research framework. The results obtained show high accuracy rates, reaching EERs as low as 0.12%, and requiring protected templates comprising 200KB.

Towards cancelable multi-biometrics based on bloom filters: a case study on feature level fusion of face and iris

In this work we propose a generic framework for generating an irreversible representation of multiple biometric templates based on adaptive Bloom filters. The presented technique enables a feature level fusion of different biometrics (face and iris) to a single protected template, improving privacy protection compared to the corresponding systems based on a single biometric trait. At the same time, a significant gain in biometric performance is achieved, confirming the sound- ness of the proposed technique.

On the vulnerability of face recognition systems towards morphed face attacks

Morphed face images are artificially generated images, which blend the facial images of two or more different data subjects into one. The resulting morphed image resembles the constituent faces, both in visual and feature representation. If a morphed image is enroled as a probe in a biometric system, the data subjects contributing to the morphed image will be verified against the enroled probe. As a result of this infiltration, which is referred to as morphed face attack, the unambiguous assignment of data subjects is not warranted, i.e. the unique link between subject and probe is annulled. In this work, we investigate the vulnerability of biometric systems to such morphed face attacks by evaluating the techniques proposed to detect morphed face images. We create two new databases by printing and scanning digitally morphed images using two different types of scanners, a flatbed scanner and a line scanner. Further, the newly created databases are employed to study the vulnerability of state-of-the-art face recognition systems with a comprehensive evaluation.

Realistic synthetic off-line signature generation based on synthetic on-line data

A novel method for the generation of synthetic offline signatures is presented. The proposed algorithm follows a two steps scheme: first, the raw synthetic dynamic functions of the synthetic signature are generated; second, several ink and paper models are applied to transform the on-line data to realistic static signatures. The novel approach is validated using four different publicly available databases both real and synthetic. The experimental protocol includes the comparison of both types of signatures in terms of: i) performance evaluation of two competitive and totally different verification systems; and ii) visual appearance according to human observers. The experimental results show the high similarity existing between synthetically generated and humanly produced samples, and the potential of the proposed method for the study of the signature trait.

Keystroke Biometrics Ongoing Competition

This paper presents the first Keystroke Biometrics Ongoing Competition (KBOC) organized to establish a reproducible baseline in person authentication using keystroke biometrics. The competition has been developed using the BEAT platform and includes one of the largest keystroke databases publicly available based on a fixed text scenario. The database includes genuine and attacker keystroke sequences from 300 users acquired in four different sessions distributed in a four month time span. The sequences correspond to the users name and surname, and therefore, each user comprises an individual and personal sequence. As baseline for KBOC, we report the results of 31 different algorithms evaluated according to accuracy and robustness. The systems have achieved EERs as low as 5.32% and high robustness to multisession variability with accuracy degradation lower than 1% for probes separated by months. The entire database is publicly available at the competition website.