Original Biometric Features Research Articles

Dual Hashing Index Cancellable Finger Vein Template Based on Gaussian Random Mapping

In the existing cancellable finger vein template protection schemes, the original biometric features cannot be well protected, which results in poor security. In addition, the performance of matching recognition performances after generating a cancellable template is poor. Therefore, a dual hashing index cancellable finger vein template protection based on Gaussian random mapping is proposed in this study. The scheme is divided into an enrollment stage and a verification stage. In the two stages, symmetric data encryption technology was used to generate encryption templates for matching. In the enrollment stage, first, the extracted finger vein features were duplicated to obtain an extended feature vector; then, this extended vector was uniformly and randomly permuted to obtain a permutation feature vector. The above two vectors were combined into a two-dimensional feature matrix. The extended and permuted feature vector made full use of the original biometric features and further enhanced the non-invertibility. Second, a random Gaussian projection vector with m×q dimensions was generated, and a random orthogonal projection matrix was generated by the Schmidt orthogonalization of the previously generated random vector. This approach accurately transferred the characteristics of the biometric features to another feature space and ensured that the biological template is revocable. Finally, the inner product of the two-dimensional feature vector and random orthogonal projection matrix was obtained and superimposed into a row. The dual index values of the largest and second largest values were repeated m times to obtain a hash code for matching. The secondary maximum value index was introduced to adjust the error generated by the random matrix, which improved the recognition rate of the algorithm. In the verification stage, another hash code for matching was generated based on symmetric data encryption technology, and then the two hash codes were cross matched to obtain the final matching result. The experimental results show that this scheme attains good recognition performance with the PolyU and SDUMLA-FV databases, that it meets the design standard for cancellable biometric identification, and that it is robust to security and privacy attacks.

A biometric cryptosystem scheme based on random projection and neural network

Several biometric cryptosystem techniques have been proposed to protect biometric templates and preserve users’ privacy. Although such techniques can thwart different attacks, it is difficult to achieve well non-linkability between biometric cryptosystems. In this paper, we propose a novel biometric cryptosystem scheme based on random projection (RP) and back propagation neural network (BPNN) to perform the task of biometric template protection. With the help of RP, an original biometric feature vector can be projected onto a fix-length feature vector of random subspace that is derived from a user-specific projection matrix. This process is revocable and produces unlinkable biometric templates. The proposed scheme further utilizes a BPNN model to bind a projected feature vector with a random key. Based on BPNN, a robust mapping between a projected feature vector and a random key is learned to generate an error-correction-based biometric cryptosystem. The security of the proposed scheme is analyzed and the experimental results on multiple biometric datasets show the feasibility and efficiency of the proposed scheme.

Cancelable Permutation-Based Indexing for Secure and Efficient Biometric Identification

Cancelable biometric schemes have been widely studied to protect templates in biometric authentication over networks. These schemes transform biometric features and perform pattern matching without restoring the original features. Although they strongly prevent the leakage of the original features, the response time can be very long in a large-scale biometric identification system. Most of the existing indexing schemes cannot be used to speed up the biometric identification system over networks since a biometric index leaks some information about the original feature. Secure and efficient indexing is a major challenge in large-scale biometric identification over networks. In this paper, we propose a novel indexing scheme that is promising with regard to both security and efficiency. The proposed indexing scheme transforms a permutation-based index, which is the state-of-the-art index in the field of similarity search, and performs a query search without recovering the original index. We also propose a method to artificially generate biometric features necessary to generate an index (which are called “pivots”) based on GANs (Generative Adversarial Networks). We prove that the transformed index leaks no information about the original index and the original biometric feature (i.e., perfect secrecy), and comprehensively show that the proposed indexing scheme has the irreversibility, unlinkability, and revocability. We then demonstrate that the proposed indexing scheme significantly outperforms the existing indexing schemes using three real datasets (face, fingerprint, and finger-vein datasets), and is very promising with respect to the accuracy and response time.

Cancelable indexing based on low-rank approximation of correlation-invariant random filtering for fast and secure biometric identification

A cancelable biometric scheme called correlation-invariant random filtering (CIRF) is known as a promising template protection scheme. This scheme transforms a biometric feature represented as an image via the 2D number theoretic transform (NTT) and random filtering. CIRF has perfect secrecy in that the transformed feature leaks no information about the original feature. However, CIRF cannot be applied to large-scale biometric identification, since the 2D inverse NTT in the matching phase requires high computational time. Furthermore, existing biometric indexing schemes cannot be used in conjunction with template protection schemes to speed up biometric identification, since a biometric index leaks some information about the original feature. In this paper, we propose a novel indexing scheme called “cancelable indexing” to speed up CIRF without losing its security properties. The proposed scheme is based on fast computation of CIRF via low-rank approximation of biometric images and via a minimum spanning tree representation of low-rank matrices in the Fourier domain. We prove that the transformed index leaks no information about the original index and the original biometric feature (i.e., perfect secrecy), and thoroughly discuss the security of the proposed scheme. We also demonstrate that it significantly reduces the one-to-many matching time using a finger-vein dataset that includes six fingers from 505 subjects.

CT patterns of acute type A aortic arch dissection: longer, higher, more anterior.

This study analysed CT patterns of the acute dissected aortic arch using original biometric features along with comparison with normal aortas. The diagnostic CT scans of 57 patients (42 males, age (mean ± SD: 64.5 ± 13.8 years) admitted with acute Stanford type A dissection involving the aortic arch were analysed by semi-automatic detection protocol of the true lumen of the dissection. We measured the distances from the apex to the ascending and descending aorta, the curvilinear length of the entire arch and of its segments (especially between the brachiocephalic artery trunk and the left subclavian artery), as well as the surface area, angle, height and shift of the arch. These measurements were compared with results previously obtained in a healthy cohort in an analysis adjusted for age, sex and weight. The surface area and rotation of the false lumen were also analysed. Compared to normal aortic arches (N), dissected aortic arches (D) were longer (D: 155 ± 26 mm, N: 135 ± 25 mm, p = 0.002), higher (D: 51 ± 10 mm, N: 45 ± 9 mm, p = 0.04), and with a more anterior apex (shift: D: 1.19 ± 0.56, N: 1.40 ± 0.62, p = 0.007). False lumen occupied between 47-65% of the aorta, turned preferentially clockwise and its rotation decreased progressively along the arch. The morphology of the dissected aortic arch differs from that of the normal arch. Thus, our compilation of aortic arch measurements may help improve existing endovascular devices and/or design of new endoprostheses. Advances in knowledge: In this article, we provide a comprehensive set of measurements of the dissected aortic arch, and show that dissected aortic arches are longer, higher, and with a more anterior apex than normal arches.

Cancellable fuzzy vault with periodic transformation for biometric template protection

Nowadays, biometrics-based authentication is playing a potential approach for many modern applications such as banking, homeland security etc. However, the end-users may feel uncomfortable to deploy this technology because of not well-solved accurate rate and security problems. To overcome these issues, some significant techniques have been proposed such as biometric template protection, reducing biometric extraction noise etc. Fuzzy vault is one of the most popular methods for biometric template security, which binds a secret key with biometric features and produces one kind of data, called the helper data, for recovering the secret key. Unfortunately, the major drawback of this approach is the lacking of cancellable property. Furthermore, most of the fuzzy vault schemes are performed on two biometrics modalities: fingerprints and iris. Some techniques were introduced to transform the original biometric feature to cancellable one. However, the computational cost of these proposals was quite large. In this research, the authors introduce a periodic transformation attached to fuzzy vault to produce the new cancellable scheme. Their transformation is not only simpler but also suitable for many kinds of biometrics modalities. The experiments demonstrate that this approach is practical with a little better error rate in comparison with the original biometric feature.

Privacy and Security Assessment of Biometric Template Protection

Abstract 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.

A Fuzzy Vault Scheme for Ordered Biometrics

The fuzzy vault scheme has recently become popular approaches to biometric template protection. Since the original scheme has been designed to work with unordered biometric features, such a scheme cannot effectively utilize order information. We present in this paper a new fuzzy vault scheme that can effectively utilize the ordered characteristics of biometric features. In this scheme, we develop ordered fuzzy vault encoding and decoding processes in order to utilize the ordered information of the features. This prevents the feature components from cross matching and reduces false acceptance ratio (FAR). Furthermore, the original biometric features (or original template) are transformed into binary features (or secure template) by random transformation. The transformed secure template provides both diversity and revocability. This transform also prevents an adversary from obtaining the original biometric template from the secure template and therefore enhance the secure level of the scheme. Based on the proposed scheme, we design an online authentication application framework implemented using face images. We compare our scheme with two contemporary approaches to verify the effectiveness of this approach. Experimental results show that our scheme is able to achieve an improved performance with several desired properties of an online authentication system.

On Random Transformations for Changeable Face Verification

The generation of changeable and privacy-preserving biometric templates is important for the pervasive deployment of biometric technology in a wide variety of applications. This paper presents a systematic analysis of random transformation-based methods for addressing the changeability and privacy problems in biometrics-based verification systems. The proposed methods transform the original biometric feature vectors using random transformations, and the sorted index numbers (SIN) of the resulting vectors in the transformed domain are stored as the biometric templates. Three types of random transformations, namely, random additive transform, random multiplicative transform, and random projection, are discussed and analyzed. The random transformations, in combination with the SIN approach, constitute repeatable and noninvertible transformations; hence, the generated templates are changeable and provide privacy protection. The effectiveness of the proposed methods is well supported by both detailed analysis and extensive experimentation on a face verification problem.

A secure biometric discretization scheme for face template protection

In this paper, a dynamic biometric discretization scheme based on Linnartz and Tuyls’s quantization index modulation scheme (LT-QIM) [Linnartz and Tuyls, 2003] is proposed. LT-QIM extracts one bit per feature element and takes care of the intra-class variation of the biometric features. Nevertheless, LT-QIM does not consider statistical distinctiveness between users, and thus lacks the capability of preserving the discriminative power of the original biometric features. We put forward a generalized LT-QIM scheme in such a way that it allocates multiple bits to each feature element according to a statistical distinctiveness measure of the feature. Hence, more bits are assigned to high distinctive features and fewer bits to low distinctive features. With provision for intra-class variation compensation and dynamic bit allocation by means of the statistical distinctiveness measure, the generalized scheme enhances the verification performance compared to the original scheme. Several comparative studies are conducted on two popular face data sets to justify the efficiency and feasibility of our proposed scheme. The security aspect is also considered by including a stolen-token scenario.

Reliable template protection technique for biometric authentication

This paper proposes a biometric template protection technique using Dynamic Quantization Transformation to convert biometrics into template of bitstring. The proposed method enables the authentication to take place in the transformed domain rather than original biometric feature space. We outline four requirements for a reliable template protection in the realm of fingerprint system. Several security and experimental analysis have been conducted to show the technique is feasible in practice.