Multi-Biometric Template Protection: Issues and Challenges

Rathgeb, Christian; Busch, Christoph

doi:10.5772/52152

Cited by 50 publications

(42 citation statements)

References 51 publications

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“…The authors summarise and discuss some characteristics of multi-biometric template protection papers, which are: (1) fusion of biometric traits in feature level, score level and decision level; (2) rearrangement of biometric representation to provide a uniform distribution of error probabilities; (3) combination of biometric modalities increases security and accuracy; (4) use of different feature extraction methods using one biometric modality; (5) use of multi-biometric protection system using multiple protected physical modalities. However, Rathgeb and Busch [6] do not discuss the use of behavioural biometric in multi-biometric protection systems. Therefore, this paper fills this gap investigating the use of multi-biometric template protection systems using behavioural modalities.…”

Section: A Related Workmentioning

confidence: 99%

“…The paper wrote by Rathgeb and Busch [6] presents a discussion about issues and challenges of multi-biometric template protection. The authors summarise and discuss some characteristics of multi-biometric template protection papers, which are: (1) fusion of biometric traits in feature level, score level and decision level; (2) rearrangement of biometric representation to provide a uniform distribution of error probabilities; (3) combination of biometric modalities increases security and accuracy; (4) use of different feature extraction methods using one biometric modality; (5) use of multi-biometric protection system using multiple protected physical modalities.…”

Section: A Related Workmentioning

confidence: 99%

“…A special case of multi-algorithmic biometric system is to use several biometric feature representations, leading to a multi-feature biometric system. Although recent progress on biometric information fusion includes the combination of auxiliary, non-discriminatory information such as userspecific characteristics and biometric sample quality [5], as well as combining multiple biometrics in the context of template protection [6], the property of using multiple protection schemes is not often not systematically evaluated, at least not in the context of information fusion.…”

Section: Introductionmentioning

confidence: 99%

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Multi-privacy biometric protection scheme using ensemble systems

Damasceno

Canuto

Poh

2015

2015 International Joint Conference on Neural Networks (IJCNN)

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Abstract-Biometric systems use personal biological or behavioural traits that can uniquely characterise an individual but this uniqueness property also becomes its potential weakness when the template characterising a biometric trait is stolen or compromised. To this end, we consider two strategies to improving biometric template protection and performance, namely, (1) using multiple privacy schemes and (2) using multiple matching algorithms. While multiple privacy schemes can improve the security of a biometric system by protecting its template; using multiple matching algorithms or similarly, multiple biometric traits along with their respective matching algorithms, can improve the system performance due to reduced intra-class variability. The above two strategies lead to a novel, ensemble system that is derived from multiple privacy schemes. Our findings suggest that, under the worst-case scenario evaluation where the key or keys protecting the template are stolen, multi-privacy protection scheme can outperform a single protection scheme as well as the baseline biometric system without template protection.

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Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-privacy biometric protection scheme using ensemble systems

Damasceno

Canuto

Poh

2015

2015 International Joint Conference on Neural Networks (IJCNN)

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“…This is also a topic of interest for biometric applications where the way templates are encoded is critical [28]. Indeed, stolen templates should not allow reconstruction of images that can be used to break in a system.…”

Section: Introductionmentioning

confidence: 99%

Reconstructing faces from their signatures using RBF regression

Mignon¹,

Jurie²

2013

Procedings of the British Machine Vision Conference 2013

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The increasing popularity of social networks raises critical issues regarding privacy and protection of personal information. One typical example -which is the motivation of the paper -is face images and face recognition, face recognition being ubiquitous on the web now. Assuming someone with bad intentions steals a database containing face signatures, would it be possible to reconstruct the corresponding face images, revealing who was in the database? Would these reconstructed images be good enough to allow them to gain access via a face recognition system? This paper brings a contribution to this topic by proposing a face reconstruction algorithm, based on RBF-regression in eigenspace, which is able to reconstruct face images from their signatures (i.e. neither knowing the identity of the persons nor their true facial appearance). We show that in addition to being visually realistic, the images generated by the proposed method can fool a state-of-the-art face recognition algorithm.

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“…Such a system is less secure than one which uses a single key with a larger number of bits. Obviously, the development of multi-biometric cryptosystems is accompanied by further issues such as common data representation, storage requirement, or feature alignment [22].…”

Section: Introductionmentioning

confidence: 99%

Unlinkable improved multi-biometric iris fuzzy vault

Rathgeb

Tams²,

Wagner

et al. 2016

EURASIP J. on Info. Security

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Iris recognition technologies are deployed in numerous large-scale nation-wide projects in order to provide robust and reliable biometric recognition of individuals. Moreover, the iris has been found to be rather stable over time, i.e. iris biometric reference data provides a strong and permanent link between individuals and their biometric traits. Hence, unprotected storage of (iris) biometric data provokes serious privacy threats, e.g. identity theft, limited re-newability, or cross-matching. Biometric cryptosystems grant a significant improvement in data privacy and increase the likelihood that individuals will effectively consent in the biometric system usage. However, the vast majority of proposed biometric cryptosystems do not guarantee desired properties of irreversibility, unlinkability, and re-newability without significantly degrading the biometric performance. In this work, we propose an unlinkable multi-instance iris biometric cryptosystem based on the improved fuzzy vault scheme. The proposed system locks biometric feature sets extracted from binary iris biometric reference data, i.e. iris-codes, of the left and right irises in a single fuzzy vault. In order to retain the size of the protected template and authentication speed, the proposed fusion step combines the most discriminative parts of two iris-codes at feature level. It is shown that the proposed key-binding process enables the generation of irreversible protected templates which prevents from previously proposed cross-matching attacks. Further, we investigate the optimal choice among potential decoding strategies with respect to biometric performance and time of key retrieval. The fully reproducible system is integrated to two different publicly available iris recognition systems and evaluated on the CASIAv3-Interval and the IITDv1 iris databases. Compared to the corresponding unprotected recognition schemes, genuine match rates of approximately 95 and 97 % at which no false accepts are observed and maintained in a single-and multi-instance scenario, respectively. Moreover, the multi-iris system is shown to significantly improve privacy protection achieving security levels of approximately 70 bits at practical biometric performance.

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Multi-Biometric Template Protection: Issues and Challenges

Cited by 50 publications

References 51 publications

Multi-privacy biometric protection scheme using ensemble systems

Multi-privacy biometric protection scheme using ensemble systems

Reconstructing faces from their signatures using RBF regression

Unlinkable improved multi-biometric iris fuzzy vault

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