Pitfall of the Detection Rate Optimized Bit Allocation within template protection and a remedy

One of the requirements of a biometric template protection system is that the protected template ideally should not leak any information about the biometric sample or its derivatives. In the literature, several proposed template protection techniques are based on binary vectors. Hence, they require the extraction of a binary representation from the real- valued biometric sample. In this work we focus on the Detection Rate Optimized Bit Allocation (DROBA) quantization scheme that extracts multiple bits per feature component while maximizing the overall detection rate. The allocation strategy has to be stored as auxiliary data for reuse in the verification phase and is considered as public. This implies that the auxiliary data should not leak any information about the extracted binary representation. Experiments in our work show that the original DROBA algorithm, as known in the literature, creates auxiliary data that leaks a significant amount of information. We show how an adversary is able to exploit this information and significantly increase its success rate on obtaining a false accept. Fortunately, the information leakage can be mitigated by restricting the allocation freedom of the DROBA algorithm. We propose a method based on population statistics and empirically illustrate its effectiveness. All the experiments are based on the MCYT fingerprint database using two different texture based feature extraction algorithms

Bio-cryptography using Zernike Moments and Key Generation by Cubic Splines

Cryptography is the process of protecting sensitive information and making it unreadable to unwanted parties. Since all algorithms that perform this task depend on the process of finding a suitable key, the key generation is considered the soul of powerful encryption. The traditionally generated keys are long and random, hence are difficult to memorize, and we need a database to store the keys. To alleviate this limitation, we use bio-cryptography that is combined of biometrics and cryptography. Using Bio-Cryptography generated keys provides the necessary security through powerful encryption and decryption of data. This paper uses cubic spline to generate a cryptographic key through extracting the features from fingerprint. The approach is based on extracting the features generated by using Zernike Moment on a biometric, and then sending these features to a Cubic-Spline Interpolator to generate the keys. A key encryption will be generated for every person through extracting the features from his / her biometric (fingerprint) and then applying these features on the cubic spline interpolator to obtain some points. These interpolated points will be used as keys to encrypt the information by using a suitable encryption algorithm.  The benefit presented by this approach is to ensure a high level of security to protect the information through generating secure keys ready to be used for unsecured channel. In this paper, we used fingerprints from Biometric Recognition Group - ATVS to examine the performance of this approach. Keywords: Biometrics, Key Generation, Zernike Moment, Cubic Spline, Cryptography, RSA, Fingerprint

On the performance of helper data template protection schemes

The use of biometrics looks promising as it is already being applied in elec- tronic passports, ePassports, on a global scale. Because the biometric data has to be stored as a reference template on either a central or personal storage de- vice, its wide-spread use introduces new security and privacy risks such as (i) identity fraud, (ii) cross-matching, (iii) irrevocability and (iv) leaking sensitive medical information. Mitigating these risks is essential to obtain the accep- tance from the subjects of the biometric systems and therefore facilitating the successful implementation on a large-scale basis. A solution to mitigate these risks is to use template protection techniques. The required protection properties of the stored reference template according to ISO guidelines are (i) irreversibility, (ii) renewability and (iii) unlinkability. A known template protection scheme is the helper data system (HDS). The fun- damental principle of the HDS is to bind a key with the biometric sample with use of helper data and cryptography, as such that the key can be reproduced or released given another biometric sample of the same subject. The identity check is then performed in a secure way by comparing the hash of the key. Hence, the size of the key determines the amount of protection. This thesis extensively investigates the HDS system, namely (i) the the- oretical classication performance, (ii) the maximum key size, (iii) the irre- versibility and unlinkability properties, and (iv) the optimal multi-sample and multi-algorithm fusion method. The theoretical classication performance of the biometric system is deter- mined by assuming that the features extracted from the biometric sample are Gaussian distributed. With this assumption we investigate the in uence of the bit extraction scheme on the classication performance. With use of the the- oretical framework, the maximum size of the key is determined by assuming the error-correcting code to operate on Shannon's bound. We also show three vulnerabilities of HDS that aect the irreversibility and unlinkability property and propose solutions. Finally, we study the optimal level of applying multi- sample and multi-algorithm fusion with the HDS at either feature-, score-, or decision-level

Improvement of fingerprint retrieval by a statistical classifier

The topics of fingerprint classification, indexing, and retrieval have been studied extensively in the past decades. One problem faced by researchers is that in all publicly available fingerprint databases, only a few fingerprint samples from each individual are available for training and testing, making it inappropriate to use sophisticated statistical methods for recognition. Hence most of the previous works resorted to simple $k$-nearest neighbor ($k$-NN) classification. However, the $k$-NN classifier has the drawbacks of being comparatively slow and less accurate. In this paper, we tackle this problem by first artificially expanding the set of training samples using our previously proposed spatial modeling technique. With the expanded training set, we are then able to employ a more sophisticated classifier such as the Bayes classifier for recognition. We apply the proposed method to the problem of one-to-$N$ fingerprint identification and retrieval. The accuracy and speed are evaluated using the benchmarking FVC 2000, FVC 2002, and NIST-4 databases, and satisfactory retrieval performance is achieved. © 2010 IEEE.published_or_final_versio

Continuous touchscreen biometrics: authentication and privacy concerns

In the age of instant communication, smartphones have become an integral part of our daily lives, with a significant portion of the population using them for a variety of tasks such as messaging, banking, and even recording sensitive health information. However, the increasing reliance on smartphones has also made them a prime target for cybercriminals, who can use various tactics to gain access to our sensitive data. In light of this, it is crucial that individuals and organisations prioritise the security of their smartphones to protect against the abundance of threats around us. While there are dozens of methods to verify the identity of users before granting them access to a device, many of them lack effectiveness in terms of usability and potential vulnerabilities. In this thesis, we aim to advance the field of touchscreen biometrics which promises to alleviate some of the recurring issues. This area of research deals with the use of touch interactions, such as gestures and finger movements, as a means of identifying or authenticating individuals. First, we provide a detailed explanation of the common procedure for evaluating touch-based authentication systems and examine the potential pitfalls and concerns that can arise during this process. The impact of the pitfalls is evaluated and quantified on a newly collected large-scale dataset. We also discuss the prevalence of these issues in the related literature and provide recommendations for best practices when developing continuous touch-based authentication systems. Then we provide a comprehensive overview of the techniques that are commonly used for modelling touch-based authentication, including the various features, classifiers, and aggregation methods that are employed in this field. We compare the approaches under controlled, fair conditions in order to determine the top-performing techniques. Based on our findings, we introduce methods that outperform the current state-of-the-art. Finally, as a conclusion to our advancements in the development of touchscreen authentication technology, we explore any negative effects our work may cause to an ordinary user of mobile websites and applications. In particular, we look into any threats that can affect the privacy of the user, such as tracking them and revealing their personal information based on their behaviour on smartphones

Enhanced Fuzzy Feature Match Algorithm for Mehndi Fingerprints

The performance of biometric system is degraded by the distortions occurred in finger print image acquisition. This paper focuses on nonlinear distortions occurred due to �Mehndi / Heena drawn on the palm/fingers. The present invention is to detect and rectify such distortions using feedback paradigm. If image is of good quality, there is no need to renovate features. So, quality of whole image is checked by generating exponential similarity distribution. Quality of local region is checked by the ridge continuity map and ridge clarity map. Then, we check whether feedback is needed or not. The desired features such as ridge structure, minutiae point, orientation, etc. are renovated using feedback paradigm. Feedback is taken from top K matched template fingerprints registered in the database. Fuzzy logic handles uncertainties and imperfections in images. For matching, we have proposed the Enhanced Fuzzy Feature Match (EFFM) for estimating triangular feature set of distance between minutiae, orientation angle of minutiae, angle between the direction of minutiae points, angle between the interior bisector of triangle and the direction of minutiae, and a minutiae type. The proposed algorithm incorporates an additional parameter minutiae type that assists to improve accuracy of matching algorithm. The experimentation on 300 Mehndi fingerprints acquired using Secugen fingerprint scanner is conducted. The results positively support EEFM for its efficiency and reliability to handle distorted fingerprints matching

Dense 3D Face Correspondence

We present an algorithm that automatically establishes dense correspondences between a large number of 3D faces. Starting from automatically detected sparse correspondences on the outer boundary of 3D faces, the algorithm triangulates existing correspondences and expands them iteratively by matching points of distinctive surface curvature along the triangle edges. After exhausting keypoint matches, further correspondences are established by generating evenly distributed points within triangles by evolving level set geodesic curves from the centroids of large triangles. A deformable model (K3DM) is constructed from the dense corresponded faces and an algorithm is proposed for morphing the K3DM to fit unseen faces. This algorithm iterates between rigid alignment of an unseen face followed by regularized morphing of the deformable model. We have extensively evaluated the proposed algorithms on synthetic data and real 3D faces from the FRGCv2, Bosphorus, BU3DFE and UND Ear databases using quantitative and qualitative benchmarks. Our algorithm achieved dense correspondences with a mean localisation error of 1.28mm on synthetic faces and detected $14$ anthropometric landmarks on unseen real faces from the FRGCv2 database with 3mm precision. Furthermore, our deformable model fitting algorithm achieved 98.5% face recognition accuracy on the FRGCv2 and 98.6% on Bosphorus database. Our dense model is also able to generalize to unseen datasets.Comment: 24 Pages, 12 Figures, 6 Tables and 3 Algorithm

Security and accuracy of fingerprint-based biometrics: A review

Biometric systems are increasingly replacing traditional password- and token-based authentication systems. Security and recognition accuracy are the two most important aspects to consider in designing a biometric system. In this paper, a comprehensive review is presented to shed light on the latest developments in the study of fingerprint-based biometrics covering these two aspects with a view to improving system security and recognition accuracy. Based on a thorough analysis and discussion, limitations of existing research work are outlined and suggestions for future work are provided. It is shown in the paper that researchers continue to face challenges in tackling the two most critical attacks to biometric systems, namely, attacks to the user interface and template databases. How to design proper countermeasures to thwart these attacks, thereby providing strong security and yet at the same time maintaining high recognition accuracy, is a hot research topic currently, as well as in the foreseeable future. Moreover, recognition accuracy under non-ideal conditions is more likely to be unsatisfactory and thus needs particular attention in biometric system design. Related challenges and current research trends are also outlined in this paper