Minutia Cylinder-code Research Articles

Identification of neutrophil extracellular trap-related genes in Alzheimer's disease based on comprehensive bioinformatics analysis.

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease. There are currently no effective interventions to slow down or prevent the occurrence and progression of AD. Neutrophil extracellular traps (NETs) have been proven to be tightly linked to AD. This project attempted to identify hub genes for AD based on NETs. Gene expression profiles of the training set and validation set were downloaded from the Gene Expression Omnibus (GEO) database, including non-demented (ND) controls and AD samples. NET-related genes (NETRGs) were collected from the literature. Differential analysis identified 21 AD differentially expressed NETRGs (AD-DE-NETRGs) majorly linked to functions such as defense response to bacterium as well as pathways including IL-17 signaling pathway, as evidenced by enrichment analyses of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Protein-protein interaction (PPI) network, Minutia Cylinder-Code (MCC) algorithm, and molecular complex detection (MCODE) algorithm in the CytoHubba plug-in were employed to identify five hub genes (NFKBIA, SOCS3, CCL2, TIMP1, ACTB). Their diagnostic ability was validated in the validation set using receiver operating characteristic (ROC) curves and gene differential expression analysis. A total of 16 miRNAs and 132 lncRNAs were predicted through the mirDIP and ENCORI databases, and a lncRNA-miRNA-mRNA regulatory network was constructed using Cytoscape software. Small molecular compounds such as Benzo(a)pyrene and Copper Sulfate were predicted to target hub genes using the CTD database. This project successfully identified five hub genes, which may serve as potential biomarkers for AD, proffering clues for new therapeutic targets.

On the security risk of pre-image attack on cancelable biometrics

Over the years, a number of biometric template protection schemes, often based on the notion of “cancelable biometrics”, have been proposed. An ideal cancelable biometric algorithm complies with four criteria: irreversibility, revocability, unlinkability, and performance preservation. Cancelable biometrics employs an irreversible but distance preserving transformation to convert the original biometric templates into protected templates. Matching in the transform domain can be accomplished due to the property of distance preservation. However, distance preservation also entails security issues, a point often overlooked in existing research. In this paper, we have conducted a comprehensive security analysis of distance preservation in cancelable biometrics for the first time. The analysis is based on a pre-image attack, which is launched to break the security of cancelable biometrics under Kerckhoffs’s assumption. Furthermore, we propose a general security analysis framework under the single and cross-transformation attacks, which also employs an information leakage estimation strategy based on mutual information as a complement. The experimental results performed on real face, iris, and fingerprint data demonstrate that the risks originating from the matching scores computed from the distance/similarity of two cancelable templates greatly compromise the security of cancelable biometric schemes, including the classic Biohashing, Index-of-max hashing, Non-linear multi-dimensional spectral hashing, Indexing-First-One hashing, Bloom Filter and Two-factor Protected Minutia Cylinder-Code. The security versus accuracy trade-off is discussed and recommendations for designing a biometric system secure against pre-image attacks are also proposed. The source code is available at github.com/biometricsecurity/CBrisks.

An IoT-Oriented Privacy-Preserving Fingerprint Authentication System

Identity authentication has become an essential component for access control in the Internet of Things (IoT) environment. To overcome the inherent weakness of password-based authentication, many present IoT devices (e.g., commercial banking smart cards) are equipped with the fingerprint authentication mechanism. However, due to the resource constraints of IoT devices, oversimplified authentication schemes are deployed, which compromise system performance significantly. Moreover, fingerprint templates in these existing schemes are unprotected. To address these issues, we propose an IoT-oriented privacy-preserving fingerprint authentication system. The proposed system is composed of four main components: 1) minutiae extraction; 2) the minutia cylinder-code (MCC)-based cancelable binary template, generated by the proposed normalized random projection; 3) the lightweight, privacy-preserving template, built by novel pairwise Boolean operations; and 4) fingerprint matching. Our system can effectively mitigate preimage and hill-climbing attacks. A prototype of the proposed system is developed using a popular open-source platform (i.e., Open Virtual Platforms). Comprehensive experimental results on eight benchmark data sets validate the effectiveness of the proposed IoT-oriented fingerprint authentication system. Our system also achieves equivalent authentication accuracy to that of the unprotected fingerprint authentication systems deployed in the resource-rich, non-IoT environment. More importantly, our system prototype is deployable to commercially available low-cost smart cards, such as Atmel AT24C256C Memory Smart Card 256K Bits. To the best of our knowledge, the proposed system is the first privacy-preserving, cancelable fingerprint authentication system developed in such a resource-constrained IoT setting.

Using Inverted Index for Fingerprint Search

Fingerprints are the most used biometric information for identifying people. With the increase in fingerprint data, indexing techniques are essential to perform an efficient search. In this work, we devise a solution that applies traditional inverted index, widely used in textual information retrieval, for fingerprint search. For that, it first converts fingerprints to text documents using techniques, such as Minutia Cylinder-Code and Locality-Sensitive Hashing, and then indexes them in inverted files. In the experimental evaluation, our approach obtained 0.42% of error rate with 10% of penetration rate in the FVC2002 DB1a data set, surpassing some established methods.

Design of cancelable MCC-based fingerprint templates using Dyno-key model

Minutia Cylinder Code (MCC) is an effective, high-quality representation of local minutia structures. MCC templates demonstrate fast and excellent fingerprint matching performance, but if compromised, they can be reverse-engineered to retrieve minutia information. In this paper, we propose alignment-free cancelable MCC-based templates by exploiting the MCC feature extraction and representation. The core component of our design is a dynamic random key model, called Dyno-key model. The Dyno-key model dynamically extracts elements from MCC’s binary feature vectors based on randomly generated keys. Those extracted elements are discarded after the block-based logic operations so as to increase security. Leveling with the performance of the unprotected, reproduced MCC templates, the proposed method exhibits competitive performance in comparison with state-of-the-art cancelable fingerprint templates, as evaluated over seven public databases, FVC2002 DB1-DB3, FVC2004 DB1 and DB2, and FVC2006 DB2 and DB3. The proposed cancelable MCC-based templates satisfy all the requirements of biometric template protection.

Alignment-Free Cross-Sensor Fingerprint Matching Based on the Co-Occurrence of Ridge Orientations and Gabor-HoG Descriptor

The existing automatic fingerprint verification methods are designed to work under the assumption that the same sensor is installed for enrollment and authentication (regular matching). There is a remarkable decrease in efficiency when one type of contact-based sensor is employed for enrolment and another type of contact-based sensor is used for authentication (cross-matching or fingerprint sensor interoperability problem). The ridge orientation patterns in a fingerprint are invariant to the sensor type. Based on this observation, we propose a robust fingerprint descriptor called the co-occurrence of ridge orientations (Co-Ror), which encodes the spatial distribution of ridge orientations. Employing this descriptor, we introduce an efficient automatic fingerprint verification method for cross-matching problem. Furthermore, to enhance the robustness of the method, we incorporate scale-based ridge orientation information through the Gabor-HoG descriptor. The two descriptors are fused with the canonical correlation analysis (CCA), and the matching score between two fingerprints is calculated using city-block distance. The proposed method is alignment-free and can handle the matching process without the need for a registration step. The intensive experiments on two benchmark databases (FingerPass and MOLF) show the effectiveness of the method and reveal its significant enhancement over the state-of-the-art methods, such as VeriFinger (a commercial SDK), minutia cylinder-code (MCC), MCC with scale, and the thin-plate spline (TPS) model. The proposed research will help security agencies, service providers, and law-enforcement departments to overcome the interoperability problem of contact sensors of different technologies and interaction types.

Deep learning compact binary codes for fingerprint indexing

With the rapid growth in fingerprint databases, it has become necessary to develop excellent fingerprint indexing to achieve efficiency and accuracy. Fingerprint indexing has been widely studied with real-valued features, but few studies focus on binary feature representation, which is more suitable to identify fingerprints efficiently in large-scale fingerprint databases. In this study, we propose a deep compact binary minutia cylinder code (DCBMCC) as an effective and discriminative feature representation for fingerprint indexing. Specifically, the minutia cylinder code (MCC), as the state-of-the-art fingerprint representation, is analyzed and its shortcomings are revealed. Accordingly, we propose a novel fingerprint indexing method based on deep neural networks to learn DCBMCC. Our novel network restricts the penultimate layer to directly output binary codes. Moreover, we incorporate independence, balance, quantization-loss-minimum, and similarity-preservation properties in this learning process. Eventually, a multi-index hashing (MIH) based fingerprint indexing scheme further speeds up the exact search in the Hamming space by building multiple hash tables on binary code substrings. Furthermore, numerous experiments on public databases show that the proposed approach is an outstanding fingerprint indexing method since it has an extremely small error rate with a very low penetration rate.

Cross-Sensor Fingerprint Matching Method Based on Orientation, Gradient, and Gabor-HoG Descriptors With Score Level Fusion

The fingerprint is one of the oldest and most widely used biometric modality for person identification. Existing automatic fingerprint matching systems perform well when the same sensor is used for both enrollment and verification (regular matching). However, their performance significantly deteriorates when different sensors are used (cross-matching, fingerprint sensor interoperability problem). We propose an automatic fingerprint verification method to solve this problem. It was observed that the discriminative characteristics among fingerprints captured with sensors of different technology and interaction types are ridge orientations, minutiae, and local multi-scale ridge structures around minutiae. To encode this information, we propose two minutiae-based descriptors: histograms of gradients obtained using a bank of Gabor filters and binary gradient pattern descriptors, which encode multi-scale local ridge patterns around minutiae. In addition, an orientation descriptor is proposed, which compensates for the spurious and missing minutiae problem. The scores from the three descriptors are fused using a weighted sum rule, which scales each score according to its verification performance. Extensive experiments were conducted using two public domain benchmark databases (FingerPass and Multi-Sensor Optical and Latent Fingerprint) to show the effectiveness of the proposed system. The results showed that the proposed system significantly outperforms the state-of-the-art methods based on minutia cylinder-code (MCC), MCC with scale, VeriFinger—a commercial SDK, and a thin-plate spline model.

Learning Binary Descriptors for Fingerprint Indexing

Fingerprint indexing is studied widely with the real-valued features, but few works focus on the binary feature descriptors, which are more appropriate to retrieve fingerprints efficiently in the large-scale fingerprint database. In this paper, the binary fingerprint descriptor (BFD), which is an effective and discriminative binary feature representation for fingerprint indexing, is proposed based on minutia cylinder code (MCC). Specifically, we first analyze MCC to find that it has characteristics of the high dimensionality, redundancy, and quantization loss. Accordingly, we propose an optimization model to learn a feature-transformation matrix, resulting in dimensionality reduction and diminishing quantization loss. Meanwhile, we also incorporate the balance, independence, and similarity-preservation properties in this learning process. Eventually, a multi-index hashing-based fingerprint indexing scheme further accelerate the exact search in Hamming space. The experiments on numerous public databases show that the BFD is discriminative and compact and that the proposed approach is outstanding for fingerprint indexing.

Design and implementation of a multibiometric system based on hand’s traits

In this paper, we present a multibiometric system based on two different biometric modalities, palmprint and fingerprint. The proposed system is based on a score fusion which can effectively combine the discriminative power of multiple biometric traits. We propose a combined fingerprint image enhancement, which achieves satisfactory results. We also apply two different indexing algorithms on fingerprints and three different indexing algorithms on palmprints. Existing multibiometric systems are not sufficiently fast for practical applications. Indexing and retrieval mechanisms can effectively narrow down the search space in large databases. We propose a fingerprint indexing based on ellipse properties, which reduces false correspondences. The proposal uses a k-means clustering algorithm to index and retrieve fingerprints and palmprints. Furthermore, we combine some minutia cylinder-code (MCC)-based algorithms and improve the MCC-based indexing algorithms. Empirical experiments on some of datasets of the national institute of standards and technology (NIST), fingerprint verification competition (FVC), and THUPALMLAB databases show that the proposed algorithm considerably narrows down the search space in fingerprint and palmprint databases and increase the recognition performance.

Speeding up and enhancing a large-scale fingerprint identification system on GPU

ABSTRACTFingerprint identification is one of the most common biometric feature problems which is used in many applications. Although state-of-the-art algorithms are very accurate, the need for fast processing a big database of millions of fingerprints is highly demanding. In this paper, we propose to adapt the fingerprint matching algorithm based on Minutia Cylinder-Code on Graphics Processing Units to speed up the matching. Another contribution of this paper is to add a consolidation stage after matching to enhance the precision. The experimental results on a GTX-680 and K40 tesla devices with standard data-sets prove that the proposed algorithm can be comparable with the state-of-the-art approach, and is suitable for a real-time identification application.

Fast exact fingerprint indexing based on Compact Binary Minutia Cylinder Codes

Abstract With explosive growth in fingerprint databases, Automatic Fingerprint Identification System has become more challenging than ever. Consequently, it is necessary to develop a fast and exact fingerprint indexing to meet the efficiency and accuracy. In this paper, learning Compact Binary Minutia Cylinder Code (CBMCC) is proposed as an effective and discriminative feature representation and Multi-Index Hashing (MIH) is suitably adopted to accelerate the exact search in fingerprint indexing field for the first time. Firstly, we analyze Minutia Cylinder Code to find that it is strongly bit-correlated and awfully unbalanced. Accordingly, we propose an optimization model to learn CBMCC with the balanced independent property and the minimal binary quantization loss. Finally, MIH method further speeds up the exact search in Hamming space by building multiple hash tables on binary code substrings. The performance test shows that CBMCC is effective and discriminative as it has the maximum intra-bit variance while the minimum inter-bit correlation. Furthermore, numerous experiments on public databases demonstrate that CBMCC–MIH is quite outstanding for fingerprint indexing since it achieves an extremely small error rate with a fairly low penetration rate.

Encrypted domain matching of fingerprint minutia cylinder-code (MCC) with l1 minimization

Fingerprint is a highly discriminative biometric modality and has gained a lot of interest in both forensic and civil applications. However, it is well known that fingerprint template stored in plaintext is vulnerable to attack. A secure fingerprint template has to be both non-invertible and non-linkable. Minutia cylinder-code (MCC) is a fixed-length and highly discriminative type of minutia descriptor. However, most of the current MCC based fingerprint matching algorithms do not possess the non-linkable property. In this paper, a secured fingerprint MCC matching scheme is proposed by utilizing l1-minimization, where the enrolled fingerprint MCC template is stored in cyphertext form (i.e., E-MCC) and is recognizable only when a close enough query fingerprint presented to the system. Our experimental results on FVC2002 DB1, FVC2002 DB2 and FVC2004 DB1 databases show that the proposed system is highly secure and accurate. The GAR with FAR=0 on FVC2002 DB1, FVC2002 DB2 and FVC2004 DB1 are 91.4%, 84.0% and 65.6%, respectively, with a security level of 33 bits. The performance of the proposed encrypted domain matching algorithm outperforms state-of-the-art fingerprint encryption algorithms.

Enhanced Locality-Sensitive Hashing for Fingerprint Forensics over Large Multi-sensor Databases

Searching the identity of an unknown fingerprint over large databases is very challenging. Minutia Cylinder-Code (MCC) has been proved to be very effective in mapping a minutiae-based representation (positions and directions only) into a set of fixed-length transformation-invariant binary vectors. Based on MCC, a Locality-Sensitive Hashing (LSH) scheme has been designed to index fingerprint in large databases, which uses a numerical approximation for the similarity between MCC vectors. However, the LSH scheme is not robust enough when there is certain distortion between template and searched samples, such as fingerprints captured by multi-sensors. In this paper, we propose a finer hash bit selection method based on LSH. Besides, we take into consideration another feature - the single maximum collision for indexing and fuse the candidate lists produced by both indexing methods to produce the final candidate list. Experimentations carried out on our collected multi-sensor database (2D and 3D databases) show that the proposed indexing approach greatly improves the performance of fingerprint indexing. Extensive evaluation was also conducted on some public benchmark databases for fingerprint indexing, and the results demonstrated that the new approach outperforms existing ones in almost all the cases.

Remote Authentication Using Vaulted Fingerprint Verification

With the rise of the Internet, remote verification of identity is an increasingly important part of modern life. From online banking systems to personal data storage to software as a service, most aspects of modern life require identity verification.Traditional authentication systems rely on the possession of a token, generally a password or smartcard. Token-based identity transactions are relatively easy to repudiate since unauthorized persons may possess the token. A system that can guarantee a user’s presence during authentication would greatly enhance the non-reputability of these transactions. Biometrics can provide this strong link between users and their identities. By measuring and comparing a feature of the user, we can increase the assurance that the user is present during authentication.Fingerprint biometrics are increasingly used for identity verification. However, these require a careful balance of accuracy and privacy that is missing in many implementations. This dissertation describes a new biometric matching system, the protection of an existing data-type, and a model for matching security with error correction codes.My research develops the Vaulted Verification (VV) system into Vaulted Fingerprint Verification (VFV) by implementing VV on a fingerprint minutia triangle representation [1].Triangle representation contains much information that enhances the accuracy of the system. VFV matcher requires no order or relation between the minutia triangles.VFV allows for key exchange and remote authentication using the challenge response protocol. Protected biometric template is used to preform the authentication. Privacy and security of the user's biometric data is preserved through multiple levels of protection. First, the system uses a protected transmission protocol to transmit the authentication token. Second, minutia triangles are difficult to invert. Third, VFV is compatible with protected minutia data types.VFV is built on blocks containing several minutia triangles. Selecting several minutia triangles within a block provides tolerance to common errors in fingerprint images. The blocks are permuted to store arbitrary data, such as encryption keys or an authenticator challenge. The data is combined with an error correcting code to provide tolerance to inter-image errors in fingerprint minutia.VFV is fully compatible with protected biometric data-types. This is demonstrated by including a protected minutia descriptor, Protected Minutia Cylinder Code (PMCC) [2]. PMCC is known for its ability to enhance the accuracy of matching fingerprint minutia while being difficult to invert. Augmented VFV features with PMCC enhance the accuracy of the system.A modification of PMCC is developed in this dissertation to enhance the privacy of the system. The PMCC's within a minutia triangle are XORed together. The XOR procedure greatly enhances the non-invertiblity of PMCC, while having a small impact on VFV accuracy.Due to the importance of error correcting codes (ECC) in VFV, a model of security with ECC is developed. It is used to identify non-trivial potential attacker uses of ECC bits.

Learning Compact Binary Codes for Hash-Based Fingerprint Indexing

Compact binary codes can in general improve the speed of searches in large-scale applications. Although fingerprint retrieval was studied extensively with real-valued features, only few strategies are available for search in Hamming space. In this paper, we propose a theoretical framework for systematically learning compact binary hash codes and develop an integrative approach to hash-based fingerprint indexing. Specifically, we build on the popular minutiae cylinder code (MCC) and are inspired by observing that the MCC bit-based representation is bit-correlated. Accordingly, we apply the theory of Markov random field to model bit correlations in MCC. This enables us to learn hash bits from a generalized linear model whose maximum likelihood estimates can be conveniently obtained using established algorithms. We further design a hierarchical fingerprint indexing scheme for binary hash codes. Under the new framework, the code length can be significantly reduced from 384 to 24 bits for each minutiae representation. Statistical experiments on public fingerprint databases demonstrate that our proposed approach can significantly improve the search accuracy of the benchmark MCC-based indexing scheme. The binary hash codes can achieve a significant search speedup compared with the MCC bit-based representation.

A High Performance Fingerprint Matching System for Large Databases Based on GPU

Fingerprints are the biometric features most used for identification. They can be characterized through some particular elements called minutiae. The identification of a given fingerprint requires the matching of its minutiae against the minutiae of other fingerprints. Hence, fingerprint matching is a key process. The efficiency of current matching algorithms does not allow their use in large fingerprint databases; to apply them, a breakthrough in running performance is necessary. Nowadays, the minutia cylinder-code (MCC) is the best performing algorithm in terms of accuracy. However, a weak point of this algorithm is its computational requirements. In this paper, we present a GPU fingerprint matching system based on MCC. The many-core computing framework provided by CUDA on NVIDIA Tesla and GeForce hardware platforms offers an opportunity to enhance fingerprint matching. Through a thorough and careful data structure, computation and memory transfer design, we have developed a system that keeps its accuracy and reaches a speed-up up to 100.8× compared with a reference sequential CPU implementation. A rigorous empirical study over captured and synthetic fingerprint databases shows the efficiency of our proposal. These results open up a whole new field of possibilities for reliable real time fingerprint identification in large databases.

Noninvertible Minutia Cylinder-Code Representation

Although several fingerprint template protection methods have been proposed in the literature, the problem is still unsolved, since enforcing nonreversibility tends to produce an excessive drop in accuracy. Furthermore, unlike fingerprint verification, whose performance is assessed today with public benchmarks and protocols, performance of template protection approaches is often evaluated in heterogeneous scenarios, thus making it very difficult to compare existing techniques. In this paper, we propose a novel protection technique for Minutia Cylinder-Code (MCC), which is a well-known local minutiae representation. A sophisticate algorithm is designed to reverse MCC (i.e., recovering original minutiae positions and angles). Systematic experimentations show that the new approach compares favorably with state-of-the-art methods in terms of accuracy and, at the same time, provides a good protection of minutiae information and is robust against masquerade attacks.

Fingerprint Indexing Based on Minutia Cylinder-Code

This paper proposes a new hash-based indexing method to speed up fingerprint identification in large databases. A Locality-Sensitive Hashing (LSH) scheme has been designed relying on Minutiae Cylinder-Code (MCC), which proved to be very effective in mapping a minutiae-based representation (position/ angle only) into a set of fixed-length transformation-invariant binary vectors. A novel search algorithm has been designed thanks to the derivation of a numerical approximation for the similarity between MCC vectors. Extensive experimentations have been carried out to compare the proposed approach against 15 existing methods over all the benchmarks typically used for fingerprint indexing. In spite of the smaller set of features used (top performing methods usually combine more features), the new approach outperforms existing ones in almost all of the cases.

Minutia Cylinder-Code: A New Representation and Matching Technique for Fingerprint Recognition

In this paper, we introduce the Minutia Cylinder-Code (MCC): a novel representation based on 3D data structures (called cylinders), built from minutiae distances and angles. The cylinders can be created starting from a subset of the mandatory features (minutiae position and direction) defined by standards like ISO/IEC 19794-2 (2005). Thanks to the cylinder invariance, fixed-length, and bit-oriented coding, some simple but very effective metrics can be defined to compute local similarities and to consolidate them into a global score. Extensive experiments over FVC2006 databases prove the superiority of MCC with respect to three well-known techniques and demonstrate the feasibility of obtaining a very effective (and interoperable) fingerprint recognition implementation for light architectures.