The cancelable multimodal template protection algorithm based on random index

Template-Protection-Techniken stellen eine wichtige Ergänzung zu biometrischen Systemen, um das gewünschte Sicherheitsniveau zu erreichen und die Anforderungen an den Schutz der Privatsphäre zu erfüllen. Diese Techniken ermöglichen Erneuerung und Widerruf der Referenzendaten in biometrischen Systemen. In dieser Arbeit wird ein systematisches Evaluierungsframework entwickelt, das sich an die wesentlichen Kriterien und Anforderungen an biometrische Template-Protection-Verfahren festhält. Seine Anwendbarkeit wird durch die Analyse der verschiedenen Algorithmen demonstriert. Die in dieser Arbeit präsentierte Evaluierung ist grundlegend für eine vollständige Analyse. Darüber hinaus ermöglicht es einen Nachweis der Sicherheit und Einhaltung der Privatsphäre. Es schafft eine Basis für Zertifizierung und Benchmark der Template-Protection-Verfahren.

In a biometric authentication system using protected templates, a pseudonymous identifier is the part of a protected template that can be directly compared. Each compared pair of pseudonymous identifiers results in a decision testing whether both identifiers are derived from the same biometric characteristic. Compared to an unprotected system, most existing biometric template protection methods cause to a certain extent degradation in biometric performance. Fusion is therefore a promising way to enhance the biometric performance in template-protected biometric systems. Compared to feature level fusion and score level fusion, decision level fusion has not only the least fusion complexity, but also the maximum interoperability across different biometric features, template protection and recognition algorithms, templates formats, and comparison score rules. However, performance improvement via decision level fusion is not obvious. It is influenced by both the dependency and the performance gap among the conducted tests for fusion. We investigate in this paper several fusion scenarios (multi-sample, multi-instance, multi-sensor, multi-algorithm, and their combinations) on the binary decision level, and evaluate their biometric performance and fusion efficiency on a multi-sensor fingerprint database with 71,994 samples.

In identity management system, commonly used biometric recognition system needs attention towards issue of biometric template protection as far as more reliable solution is concerned.In view of this biometric template protection algorithm should satisfy security, discriminability and cancelability.As no single template protection method is capable of satisfying the basic requirements, a novel technique for face template generation and protection is proposed.The novel approach is proposed to provide security and accuracy in new user enrollment as well as authentication process.This novel technique takes advantage of both the hybrid approach and the binary discriminant analysis algorithm.This algorithm is designed on the basis of random projection, binary discriminant analysis and fuzzy commitment scheme.Three publicly available benchmark face databases are used for evaluation.The proposed novel technique enhances the discriminability and recognition accuracy by 80% in terms of matching score of the face images and provides high security.

Biometric template protection is one of the important issues in deploying a practical biometric system. To tackle this problem, many algorithms have been reported in recent years, most of them being applicable to fingerprint biometric. Since the content and representation of fingerprint template is different from templates of other modalities such as face, the fingerprint template protection algorithms cannot be directly applied to face template. Moreover, we believe that no single template protection method is capable of satisfying the diversity, revocability, security and performance requirements. We propose a three-step cancelable framework which is a hybrid approach for face template protection. This hybrid algorithm is based on the random projection, class distribution preserving transform and hash function. Two publicly available face databases, namely FERET and CMU-PIE, are used for evaluating the template protection scheme. Experimental results show that the proposed method maintains good template discriminability, resulting in good recognition performance. A comparison with the recently developed random multispace quantization (RMQ) biohashing algorithm shows that our method outperforms the RMQ algorithm.

Considering that the information representation among multimodal biometric is complementary and the necessity of privacy-sensitive information protection over cloud and Internet of Things, this paper presents a secure template protection algorithm for bimodal face images using full-type quaternion generic polar complex exponential transform and random projection. The bimodal face images are firstly encoded into a full quaternion matrix and quaternion generic polar complex exponential transform is used for generating primary features. Then sparse random projection is followed aiming at making the fused features non-invertibility and be able to reissued. In additionally, the generalized discriminant analysis is employed to reduce dimension of the selected features. Experimental results obtained on three face datasets have demonstrated that the proposed method presents good recognition performance in comparison with other existing methods, while it can ensure the secrecy and privacy of facial images.

Securing templates in a face recognition system using Error-Correcting Output Code and chaos theory

With built-in artificial intelligence (AI), edge devices, e.g., smart cameras, can perform tasks like detecting and tracking individuals, which is referred to as edge biometrics. As a driving force for AI, machine/deep learning plays a critical role in edge biometrics. Machine/deep learning based edge biometric systems outperform their nonmachine learning counterpart. However, research shows that artificial neural networks, e.g., convolutional neural networks, are invertible such that adversaries can obtain a certain amount of information about the original inputs/templates. This information leakage is not tolerable for biometric systems because biometric data in the original (raw) templates cannot be reset or replaced. Once compromised, they are lost forever. Therefore, how to prevent original biometric templates from being attacked through inverting deep neural networks is a pressing, but unsolved issue, for deep learning based biometric recognition. To address the issue, in this paper, we develop a novel biometric template protection algorithm using the binary decision diagram (BDD) for deep learning based finger-vein biometric systems. The proposed algorithm is capable of creating a new noninvertible version of the original finger-vein template, which is stacked with an artificial neural network–the multilayer extreme learning machine (ML-ELM) to generate a privacy-preserving finger-vein recognition system, named BDD-ML-ELM. The proposed BDD-ML-ELM ensures the safety of the original finger-vein template even if its transformed version is compromised. The transformed template, if compromised, can be revoked and replaced with another new version by simply changing the user-specific keys. Therefore, the BDD-ML-ELM has a clear advantage over the existing machine/deep learning based biometric systems, whose raw biometric templates are vulnerable when the artificial neural network suffers an inversion attack.

Iris biometrics are one of the strongest modalities in terms of performance, and as such, are used in several high-security scenarios. However, remote/network authentication creates its own class of problems that have not previously been considered. In this work, we adapt Vaulted Verification, a novel remote matching protocol, to work with iris biometrics in a network authentication setting. We give an overview of how Vaulted Verification works and show how to adapt it to the unique challenges of iris biometrics. We then investigate the algorithm's security and evaluate its performance, showing that Vaulted Verification is among the top-performing template protection algorithms for the CASIAv1 dataset. We conclude with a summary and ideas for future work.