Minutiae Points Research Articles

A non‐invertible transformation based technique to protect a fingerprint template

AbstractA fingerprint‐based authentication system provides security to the applications of numerous fields and usually stores minutiae information in the database as a template. It has been observed from the literature that the reconstruction of an original fingerprint is possible from minutiae points information; hence the security of the stored template becomes extremely crucial. Cancellable biometric techniques based on non‐invertible transformation protect the stored template. These techniques prevent the reconstruction of original fingerprint data from the compromised template and avoid unauthorized access to the system. In this paper, a technique based on the non‐invertible transformation to protect a fingerprint template is proposed. In the technique, minutiae locations in a fingerprint are transformed by using the minutiae's original locations and orientation information, and a user keyset. A principal component analysis based approach to align the probe and gallery templates of fingerprint images while matching is also proposed. The evaluation of the proposed technique is carried out on seven different fingerprint databases taken from FVC2000, FVC2002, and FVC2004 databases, and its performance is compared with other existing state‐of‐the‐art techniques in the literature. The comparative performance shows that the proposed technique is highly robust and performs exceptionally well compared to other existing techniques.

Fingerprint feature data matching algorithm based on distributed computing

In order to effectively reduce the running time of fingerprint feature data matching algorithm and improve the matching accuracy. This paper designs and proposes a fingerprint feature data equivalent matching algorithm based on distributed computing. In the process of de-counterfeiting, the method of combining distance de-counterfeiting with structure de-counterfeiting is adopted. The information of minutiae feature points for subsequent matching, sampling information of corresponding ridge and direction of minutiae points are obtained by ridge tracking method. On this basis, use the fingerprint matching algorithm based on the congruent triangle to match the extracted minutiae feature points, so as to achieve the purpose of matching fingerprint feature data equally. The simulation results show that the proposed algorithm can effectively reduce the running time and improve the matching accuracy, which fully verifies the practical application value of the algorithm.

A novel fingerprint template protection and fingerprint authentication scheme using visual secret sharing and super-resolution

Fingerprint is the most recommended and extensively practicing biometric trait for personal authentication. Most of the fingerprint authentication systems trust minutiae as the characteristic for authentication. These characteristics are preserved as fingerprint templates in the database. However, it is observed that the databases are not secure and can be negotiated. Recent studies reveal that, if a person’s minutiae points are dripped, fingerprint can be restored from these points. Similarly, if the fingerprint records are lost, it is a permanent damage. There is no mechanism to replace the fingerprint as it is part of the human body. Hence there is a necessity to secure the fingerprint template in the database. In this paper, we introduce a novel fingerprint template protection and fingerprint authentication scheme using visual secret sharing and super-resolution. During enrollment, a secret fingerprint image is encrypted into n shares. Each share is stored in a distinct database. During authentication, the shares are collected from various databases. The original secret fingerprint image is restored using a multiple image super-resolution procedure. The experimental results show that the reconstructed fingerprints are similar to the original fingerprints. The proposed method is robust, secure, and efficient in terms of fingerprint template protection and authentication.

Biometric template security using DNA codec based transformation

Feature transformation is the most primary step in biometric template protection whose effectiveness is directly dependent upon the test and trained samples. The minutiae points are segregated to retrieve the attributes with the singular point data to enlarge the secure user template. The user fingerprint information could be revealed to produce the secured template with the help of the fingerprint image. An attempt of feature transformation is experimented in this paper by DNA based encoding methodology. The general formation of the proposed technique is constructed from the Biometric data to generate the feature extraction; the Z pattern generation is used for implementing the transformation and produced the DNA codec. The evaluation of each step in the proposed scheme is theoretically evaluated and experimented with. The revocability, diversity and the security parameters are analyzed and compared with the relevant methods using the images from FVC2004 dataset that the accuracy for biometric template is achieved through the enhanced recognition rate. The complexity and the performance of the proposed method proved challenging to the state of art techniques.

Reducing computational complexity in fingerprint matching

The performance of cross-correlation functions can decrease computational complexity under optimal fingerprint feature selection. In this paper, a technique is proposed to perform alignment of fingerprints followed by their matching in fewer computations. Minutiae points are extracted and alignment is performed on the basis of their spatial locations and orientation fields. Unlike traditional cross-correlation based matching algorithms, ridges are not included in the matching process to avoid redundant computations. However, optimal cross-correlation is chosen by correlating feature vectors accompanying x-y locations of minutiae points and their aligned orientation fields. As a result, matching time is significantly reduced with much improved accuracy.

PALMPRINT RECOGNITION

Palmprint is an example of physiological characteristics of an individual, which can be easily captured by using some types of sensors and cameras. The palmprint has many nature compositions, which contain rich features that mainly used for distinguishing such as, wrinkles, ridges, principal lines, singular, and minutiae points. These make a palmprint as one of a unique biometric and reliable for human recognition. In this work have used multiple correlation filters per class for performing palmprint classification algorithm

Robust biometric authentication system with a secure user template

Biometric feature based human authentication systems provide various advantages over the classical authentication systems. Out of numerous biometric features in a human body, the fingerprint is the most commonly used biometric feature for the authentication of a person. Typically, the fingerprint-based systems rely on minutiae points information and use it directly as a user template. Several studies have shown that the use of minutiae points directly as a user template is unreliable as it may lead to the reconstruction of the original fingerprint. As databases are vulnerable to attacks, it is important to secure fingerprint information. In this paper, we propose a technique in which a strong 3-dimensional template is created from the fingerprint of a user. From the minutiae points of the fingerprint, minutiae triplets are computed, through which a secured user template is generated. The user template developed using the proposed technique is found to be robust and shows good revocability, diversity, security, and performance. The experimental results obtained on various fingerprint databases are extremely encouraging and demonstrate the effectiveness of the proposed technique.

Vein Pattern Verification and Identification Based on Local Geometric Invariants Constructed from Minutia Points and Augmented with Barcoded Local Feature

This paper presents the development of a hybrid feature—dorsal hand vein and dorsal geometry—modality for human recognition. Our proposed hybrid feature extraction method exploits two types of features: dorsal hand geometric-related and local vein pattern. Using geometric affine invariants, the peg-free system extracts minutia points and vein termination and bifurcation and constructs a set of geometric invariants, which are then used to establish the correspondence between two sets of minutiae—one for the query vein image and the other for the reference vein image. When the correspondence is established, geometric transformation parameters are computed to align the query with the reference image. Once aligned, hybrid features are extracted for identification. In this study, the algorithm was tested on a database of 140 subjects, in which ten different dorsal hand geometric-related images were taken for each individual, and yielded the promising results. In this regard, we have achieved an equal error rate (EER) of 0.243%, indicating that our method is feasible and effective for dorsal vein recognition with high accuracy. This hierarchical scheme significantly improves the performance of personal verification and/or identification.

A multimodal biometric identification system based on cascade advanced of fingerprint, fingervein and face images

<p>In this paper, we present a multimodal biometric recognition system that combines fingerprint, fingervein and face images based on cascade advanced and decision level fusion. First, in fingerprint recognition system, the images are enhanced using gabor filter, binarized and passed to thinning method. Then, the minutiae points are extracted to identify that an individual is genuine or impostor. In fingervein recognition system, image processing is required using Linear Regression Line, Canny and local histogram equalization technique to improve better the quality of images. Next, the features are obtained using Histogram of Oriented Gradient (HOG). Moreover, the Convolutional Neural Networks (CNN) and the Local Binary Pattern (LBP) are applied to detect and extract the features of the face images, respectively. In addition, we proposed three different modes in our work. At the first, the person is identified when the recognition system of one single biometric modality is matched. At the second, the fusion is achieved at cascade decision level method based on AND rule when the recognition system of both biometric traits is validated. At the last mode, the fusion is accomplished at decision level method based on AND rule using three types of biometric. The simulation results have demonstrated that the proposed fusion algorithm increases the accuracy to 99,43% than the other system based on unimodal or bimodal characteristics.</p>

Fingerprint image enhancement and reconstruction using the orientation and phase reconstruction

Fingerprints are the one of the most important means in the forensics as a means of identification of the criminals owning to the uniqueness and the distinct features in them. Fingerprint identification is considered as an important means for the identification of the people around the globe. Minutiae are the details present in the human fingerprints which are used as a means of identification and verification. Minutiae are the distinctive points which can be used for the effective reconstruction of the fingerprint image. However, there was a limitation that was considered. The minutiae points are completely not enough for reconstruction of the image. Many spurious minutiae are not included and the results for the latent fingerprints are not as accurate as they are for the normal data sets. In this paper, a novel technique has been proposed which considers the minutiae density and the orientation field direction for the reconstruction of the fingerprint. Two public domain databases Fingerprint verification competition 2002 (FVC2002) and fingerprint verification competition 2004 (FVC2004) have been used for the experimental results and to validate the suggested methods for the fingerprint reconstruction and enhancement.

Reversible Watermarking Technique for Fingerprint authentication based on DCT

In this paper, a new reversible and blind fingerprint image watermarking technique based on the differential method and discrete cosine transform (DCT) domains is presented. The focus is to increase the security of the fingerprint image in authentication systems. Two DCT-transformed sub-vectors are employed to embed the bits of the watermark sequence in a differential scheme. The original sub-vectors are acquired by the DCT transform on the host fingerprint image. In the extraction process, a minor variance between the sub-vectors that correspond to the watermarked fingerprint image directly allows access to the embedded watermark sequence; therefore, the extraction process doesn’t require an original fingerprint. The original fingerprint image is then recovered from the watermarked fingerprint image based on the reversible watermarking technique. The similarity between the reversible fingerprint image and the original is considered, and we could extract minutiae points from it without a problem. The proposed technique is evaluated using 80 fingerprint images from 10 persons for each from FVC2002 fingerprint database. Eight fingerprint images have been taken from each person to be used as the template then the process was followed by embedding the watermark into each fingerprint image. The experimental results validate the proposed method able to give promising results in preserving the fingerprint image security.

Finger-Vein Pattern Restoration With Generative Adversarial Network

Finger-vein recognition technology has attracted more and more attention because of its high security and convenience. However, the finger-vein image capturing is affected by various factors, which results that some vein patterns are missed in acquired image. Matching minutiae features in such images ultimately degrades verification performance of the finger-vein recognition system. To overcome this problem, in this paper, a novel finger-vein image restoration approach is proposed to recover the missed patterns based on generative adversarial network (GAN), as the first attempt in this area. Firstly, we employ the segmentation algorithm to extract finger-vein network, which is further subject to thinning operation. Secondly, the resulting thinning image is taken as an input of a GAN model to restore the missed vein patterns. Thirdly, the minutiae points are extracted from restoration finger-vein pattern. Finally, we propose a matching approach for verification. Experimental results show that the proposed method can restore the missed vein pattern and reduce the equal error rate (EER) of the finger-vein verification system.

Palmprint Recognition using Robust Template Matching

Palmprint recognition system is extensively deployed in a variety of applications, ranging from forensic to mobile phones. This paper proposed a non-invertible palmprint template that stores the relative geometric information of the minutiae points. The information about the minutiae orientation and their location are not available in the template, which makes impossible to estimate the orientation of the palmprint from the template. The proposed template is robust to rotation and scaling and immune to reconstruction algorithm. Finally, Delaunay triangulation based internal angle matching which is computationally efficient is used for template matching. The proposed method is tested on standard PolyU, IIT-Delhi and Multi-spectral palm-print databases and yields better results in terms of Correct Recognition Rate (CRR) and Equal Error Rate (EER) of 95.4% and 0.37% respectively.

Non-Invertible cancellable fingerprint template for fingerprint biometric

Fingerprint biometric has emerged as a highly successful biometric recognition system and find its application in the diverse domain. The fingerprint template database needs to be protected from unauthorized access. If an adversary has acquired the unprotected fingerprint template, then he/she may reconstruct original fingerprint which can be used for unauthorized access of all the systems where the same fingerprint has been used. Hence, securing the fingerprint template is required. In this paper, a non-invertible cancellable fingerprint template based on information extracted from the Delaunay triangulation of minutiae points is proposed. For making the template cancellable a user-specific key of a random binary string is used. The proposed template is revocable, diverse, secure and also gives a good recognition accuracy. The proposed template is tested with a standard FVC2002 database and gives good results as compared with the latest methods.

Optimisation of Point-Set Matching Model for Robust Fingerprint Verification in Changing Weather Conditions

Aims: To provide a baseline for the configuration of Automated Fingerprint Verification System (AFVS) in the face of changing weather and environmental conditions in order to ensure performance accuracy. 
 Study Design: Statistical and theoretical research approaches.
 Place and Duration of Study: Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria, between July 2017 and July 2018.
 Methodology: Data set were collected in the South-South geopolitical zone of Nigeria. We use 10,000 minutiae points defined by location and orientation features extracted from fingerprint samples obtained at 9 various physical and environmental conditions over 12 months period. These data were used to formulate linear regression models that were used as constraints to the verification objective function derived as constrained linear least squares. The effects of the changing weather and environmental conditions were incorporated into the optimised point-set matching model in order to minimise the total relative error on location and orientation differences between pairs of minutiae. The model was implemented using interior-point convex quadratic programming was implemented in Matlab.
 Results: The results obtained from the optimisation function by adjusting the thresholds of the effects of weather and environmental conditions to 0.0, 0.0 for location and orientation properties of minutiae, respectively, showed minimal total relative errors on the corresponding pairs of matched minutiae, when compared with using the default threshold values of the selected conditions.
 Conclusion: The optimisation of point-set based model could provide a computational basis for accurate fingerprint verification for low and high-security AFVS in unfavourable conditions if they are incorporated into the matching model. However, further validation and evaluation of the model with data sets from regions with similar weather and environmental conditions is needed to further validate its robustness in terms of performance accuracy

Feature Extraction Methods for Palmprint Recognition: A Survey and Evaluation

Palmprint processes a number of unique features for reliable personal recognition. However, different types of palmprint images contain different dominant features. Instead, only some features of the palmprint are visible in a palmprint image, whereas the other features may not be notable. For example, the low-resolution palmprint image has visible principal lines and wrinkles. By contrast, the high-resolution palmprint image contains clear ridge patterns and minutiae points. In addition, the three dimensional (3-D) palmprint image possesses curvatures of the palmprint surface. So far, there is no work to summarize the feature extraction of different types of palmprint images. In this paper, we have an aim to completely study the feature extraction and recognition of palmprint. We propose to use a unified framework to classify palmprint images into four categories: 1) the contact-based; 2) contactless; 3) high-resolution; and 4) 3-D palmprint images. Then, we analyze the motivations and theories of the representative extraction and matching methods for different types of palmprint images. Finally, we compare and test the state-of-the-art methods via the widely used palmprint databases, and point out some potential directions for future research.

Level 2 feature extraction for latent fingerprint enhancement and matching using type-2 intuitionistic fuzzy set

Latent fingerprints are acquired from crime places which are utilised to distinguish suspects in crime inspection. In general, latent fingerprints contain mysterious ridge and valley structure with nonlinear distortion and complex background noise. These lead to fundamentally difficult problem for further analysis. Hence, the image quality is required for matching those latent fingerprints. In this work, we develop a model, which needs manually marked region of interest latent fingerprints for enhancement and matching. The proposed model includes two phases: i) latent fingerprints contrast enhancement using intuitionistic fuzzy set; ii) extract the level 2 feature (minutiae) from the latent fingerprint image. This technique is functioned depend on minutia points which investigate n number of images and the Euclidean distance is applied for calculate the matching scores. We tested our algorithm for matching, using some public domain fingerprint databases such as fingerprint verification competition-2004 and Indraprastha Institute of Information Technology-latent fingerprint, which indicates that by fusing the proposed enhancement algorithm, the matching precision has fundamentally, moved forward.

Latent Palm Print Matching Based on Minutiae Features for Forensic Applications

In forensic application, palm print recognition receives significant attention because of the developing live scan palm print technology. In most forensic applications, the critical evidence obtained is those of latent palm prints. About 30% of the latent obtained from the crime scenes are of palms. Palm prints has large area of foreground, which consists of 1000’s of minutiae points. Hence latent palm print matching requires development of novel strategies. Here we design a palm print matching strategy which is based on minutiae clustering. The minutiae match propagation is also considered for the minutiae correspondence. Minutiae clusters are formed based on the local minutiae features. Each cluster contains minutiae that contain similar local features. The correspondence between two palm print is done within the clusters. Mated minutiae pairs are considered by the Minutiae match propagation algorithm to determine the correspondence between the query palm print and the full palm print. Starting with the initial minutiae pair, the algorithm checks for further matches with the full palm print image

Fingerprint growth model for mitigating the ageing effect on children’s fingerprints matching

Nowadays, the majority of fingerprint quality, matching and feature extraction algorithms are developed and trained on fingerprints of adults. Accordingly, the processing of children’s fingerprints presents performance issues derived for the most part from: (1) their smaller size and finer ridge structure; (2) their higher variability over time due to the displacement of minutiae induced by growth. The present article is focused on the second factor. The rapid growth of children fingerprints causes a significant displacement of the minutiae points between samples of the same finger acquired with a few years distance from each other. This displacement results in a decrease of the accuracy of fingerprint recognition systems when the reference and probe sample drift apart in time. This effect is known as biometric ageing. In the present study we propose to address this issue by developing and validating a minutiae-based growth model, derived from a database of over 60,000 children’s fingerprints, acquired in real operational conditions, ranging between 5 and 16 years of age, with a time difference between fingerprint pairs re-enrolments of up to 6 years. We analyze two potential application scenarios for the developed growth model. On one hand, we use the model to grow children’s fingerprints in order to spread out the minutiae points to attain sizes similar to those of a sample captured at a later point in time. On the other hand, we apply the model to rejuvenate fingerprints enrolled at a later stage by contracting the minutiae points so that their location is more similar to those of a sample acquired earlier. In both scenarios, the application of the growth model to produce artificially grown/rejuvenated fingerprint minutiae templates results in a significant improvement of the matching scores compared to the ones produced by original fingerprints.

Fixed-length asymmetric binary hashing for fingerprint verification through GMM-SVM based representations

Fingerprint minutiae information is an unordered and variable-sized collection of minutiae locations and orientations. Advanced template protection algorithms which require a fixed-length binary template cannot operate on minutiae points. In this paper, we propose a novel framework that provides practical solutions that can be used in developing secure fingerprint verification systems. The framework, by using a GMM-SVM fingerprint representation scheme, first generates fixed-length feature vectors from minutiae point sets. The fixed-length representation enables the application of modern cryptographic alternatives based on homomorphic encryption to minutiae template protection. Our framework then utilizes an asymmetric locality sensitive hashing (ALSH) in order to convert the generated fixed-length but real valued GMM-SVM feature vector to a binary bit string. This binarization step transforms the matching process to calculating Hamming distance between binary vectors and expedites fingerprint matching. The verification performance of the framework is evaluated on FVC2002DB1A and DB2A databases.