nils-herrmann/clean_scirepeval huggingface dataset:
Security and Accuracy Trade-off in Anonymous Fingerprint Recognition

The security, lack of anonymity and revocability of the biometric template are critical issues that need to be addressed in order to vindicate the viability of biometric based authentication systems. Several methods have been proposed to address these problems. However, most of these methods offer lower accuracies than the base system where the template is insecure. This is because in most systems the gain in the security is achieved as a result of loss in non-redundant information. In this paper, we describe tradeoff between accuracy of an anonymous system and the security of the biometrics system. As a case study we start with a highly secure representation of a fingerprint. Then we describe several methods and show experimental results proving that every time we add more information to the secure representation, the accuracy increases, however valuable information is revealed to an adversary. We propose, via a k-trial attack model, how the trade-off can be complemented by another security policy such as an account lockout after a given number of attempts.
A Real-Time Matching System for Large Fingerprint Databases

With the current rapid growth in multimedia technology, there is an imminent need for efficient techniques to search and query large image databases. Because of their unique and peculiar needs, image databases cannot be treated in a similar fashion to other types of digital libraries. The contextual dependencies present in images, and the complex nature of two-dimensional image data make the representation issues more difficult for image databases. An invariant representation of an image is still an open research issue. For these reasons, it is difficult to find a universal content-based retrieval technique. Current approaches based on shape, texture, and color for indexing image databases have met with limited success. Further, these techniques have not been adequately tested in the presence of noise and distortions. A given application domain offers stronger constraints for improving the retrieval performance. Fingerprint databases are characterized by their large size as well as noisy and distorted query images. Distortions are very common in fingerprint images due to elasticity of the skin. In this paper, a method of indexing large fingerprint image databases is presented. The approach integrates a number of domain-specific high-level features such as pattern class and ridge density at higher levels of the search. At the lowest level, it incorporates elastic structural feature-based matching for indexing the database. With a multilevel indexing approach, we have been able to reduce the search space. The search engine has also been implemented on Splash 2-a field programmable gate array (FPGA)-based array processor to obtain near-ASIC level speed of matching. Our approach has been tested on a locally collected test data and on NIST-9, a large fingerprint database available in the public domain.
Evaluation techniques for biometrics-based authentication systems (FRR)

Biometrics-based authentication is becoming popular because of increasing ease-of-use and reliability. Performance evaluation of such systems is an important issue. We attempt to address two aspects of performance evaluation that have been conventionally neglected. First, the "difficulty" of the data that is used in a study influences the evaluation results. We propose some measures to characterize the data set so that the performance of a given system on different data sets can be compared. Second, conventional studies often have reported the false reject and false accept rates (FRR, FAR) in the form of match score distributions. However, no confidence intervals are computed for these distributions, hence no indication of the significance of the estimates is given. In this paper, we systematically study and compare parametric and nonparametric (bootstrap) methods for measuring confidence intervals. We give special attention to false reject rate estimates.
Face anti-spoofing with multifeature videolet aggregation

Biometric systems can be attacked in several ways and the most common being spoofing the input sensor. Therefore, anti-spoofing is one of the most essential prerequisite against attacks on biometric systems. For face recognition it is even more vulnerable as the image capture is non-contact based. Several anti-spoofing methods have been proposed in the literature for both contact and non-contact based biometric modalities often using video to study the temporal characteristics of a real vs. spoofed biometric signal. This paper presents a novel multi-feature evidence aggregation method for face spoofing detection. The proposed method fuses evidence from features encoding of both texture and motion (liveness) properties in the face and also the surrounding scene regions. The feature extraction algorithms are based on a configuration of local binary pattern and motion estimation using histogram of oriented optical flow. Furthermore, the multi-feature windowed videolet aggregation of these orthogonal features coupled with support vector machine-based classification provides robustness to different attacks. We demonstrate the efficacy of the proposed approach by evaluating on three standard public databases: CASIA-FASD, 3DMAD and MSU-MFSD with equal error rate of 3.14%, 0%, and 0%, respectively.
Cancelable iris biometric

A person only has two irises - if his pattern is stolen he quickly runs out of alternatives. Thus methods that protect the true iris pattern need to be adopted in practical biometric applications. In particular, it is desirable to have a system that can generate a new unique pattern if the one being used is lost, or generate different unique patterns for different applications to prevent cross-matching. For backwards compatibility, these patterns should look like plausible irises so they can be handled with the same processing tools. However, they should also non-invertibly hide the true biometric so it is never exposed, or even stored. In this paper four such ldquocancelablerdquo biometric methods are proposed that work with conventional iris recognition systems, either at the unwrapped image level or at the binary iris code level.
Matching cross-resolution face images using co-transfer learning

Face recognition systems, trained in controlled environment, often fail to efficiently match low resolution images with high resolution images. In this research, a co-transfer learning framework is proposed in which knowledge learnt in controlled high resolution environment is transferred for matching low resolution probe images with high resolution gallery. The proposed framework seamlessly combines transfer learning and co-training to perform knowledge transfer by updating classifier's decision boundary with low resolution probe instances. Experiments are performed on the CMU-Multi-PIE and SCface database with gallery images of size 72 × 72 and size of probe images varying from 48 × 48 to 16 × 16. The results show that, in terms of rank-1 identification accuracy, the proposed algorithm outperforms existing approaches by at least 5%.
A Gradient Descent Approach for Multi-modal Biometric Identification

While biometrics-based identification is a key technology in many critical applications such as searching for an identity in a watch list or checking for duplicates in a citizen ID card system, there are many technical challenges in building a solution because the size of the database can be very large (often in 100s of millions) and the intrinsic errors with the underlying biometrics engines. Often multi-modal biometrics is proposed as a way to improve the underlying biometrics accuracy performance. In this paper, we propose a score based fusion scheme tailored for identification applications. The proposed algorithm uses a gradient descent method to learn weights for each modality such that weighted sum of genuine scores is larger than the weighted sum of all the impostor scores. During the identification phase, top K candidates from each modality are retrieved and a super-set of identities is constructed. Using the learnt weights, we compute the weighted score for all the candidates in the superset. The highest scoring candidate is declared as the top candidate for identification. The proposed algorithm has been tested using NIST BSSR-1 dataset and results in terms of accuracy as well as the speed (execution time) are shown to be far superior than the published results on this dataset.
An evaluation of error confidence interval estimation methods

Reporting the accuracy performance of pattern recognition systems (e.g., biometrics ID system) is a controversial issue and perhaps an issue that is not well understood. This work focuses on the research issues related to the oft used confidence interval metric for performance evaluation. Using a biometric (fingerprint) authentication system, we estimate the false reject rates and false accept rates of the system using a real fingerprint dataset. We also estimate confidence intervals of these error rates using a number of parametric and non-parametric (e.g., bootstrapping) methods. We attempt to assess the accuracy of the confidence intervals based on estimate and verify strategy applied to repetitive random train/test splits of the dataset. Our experiments objectively verify the hypothesis that the traditional bootstrap and parametric estimate methods are not very effective in estimating the confidence intervals and magnitude of interdependence among data may be one of the reasons for their ineffective estimates. Further, we demonstrate that the resampling the subsets of the data samples (inspired from moving block bootstrap) may be one way of replicating interdependence among the data; the bootstrapping methods using such subset resampling may indeed improve the accuracy of the estimates. Irrespective of the method of estimation, the results show that the (1-/spl alpha/) 100% confidence intervals empirically estimated from the training set capture significantly smaller than (1-/spl alpha/) fraction of the estimates obtained from the test set.
QPLC: A novel multimodal biometric score fusion method

In biometrics authentication systems, it has been shown that fusion of more than one modality (e.g., face and finger) and fusion of more than one classifier (two different algorithms) can improve the system performance. Often a score level fusion is adopted as this approach doesn't require the vendors to reveal much about their algorithms and features. Many score level transformations have been proposed in the literature to normalize the scores which enable fusion of more than one classifier. In this paper, we propose a novel score level transformation technique that helps in fusion of multiple classifiers. The method is based on two components: quantile transform of the genuine and impostor score distributions and a power transform which further changes the score distribution to help linear classification. After the scores are normalized using the novel quantile power transform, several linear classifiers are proposed to fuse the scores of multiple classifiers. Using the NIST BSSR-1 dataset, we have shown that the results obtained by the proposed method far exceed the results published so far in the literature.
Comparative analysis of registration based and registration free methods for cancelable fingerprint biometrics

Cancelable biometric systems are gaining in popularity for use in person authentication for applications where the privacy and security of biometric templates are important considerations. A variety of approaches have been proposed in the literature. In this work, we have chosen two (a registration based and a registration free) techniques and performed a comparative study focusing on template representation size, useful dataset coverage, system accuracy and transform strength. Results show that both systems have their own advantages that are suited for use in specific applications.
Learning partitioned least squares filters for fingerprint enhancement

Fingerprint images contain varying amount of noise because of the limitations of the fingerprint acquisition process. It is often necessary to enhance such noisy fingerprint images so that the features extracted from them are reliable. We propose a novel approach to fingerprint enhancement where a set of filters are learned using the "learn-from-example" paradigm. An expert provides the ground truth information for ridges in a small set of representative fingerprint images. The space of local fingerprint patterns in a small neighborhood is partitioned into a set of expressive yet computationally simple classes. A filter is learnt for each partition by finding the optimal linear mapping (in least-square sense) from the input to the enhanced space. The proposed approach offers distinct performance and speed advantages for a wide variety of fingerprint images.
Redundancy and diversity measure inspired biometrics fusion

In many identification problems, finding a duplicate using biometrics is very challenging task because of the size of the database and the errors related to the core biometrics engine. Fusion of different modes of biometric system can lead to improved recognition accuracy. In a multi classifier fusion scenario, the confidence of one classifier should not only depend on its own decision confidence but also on the diffidence of other classifiers. We propose a novel solution which uses a machine learning approach to generate confidence scores for each system for every instance of decision by implicitly modelling the redundancy (all classifiers making the same decision) and diversity (each classifier making a different decision and only a subset of classifiers is right at one time)from the training data. These confidence scores are used as weights for votes and the final decision is made using weighted sum of votes. Experimental results are provided by comparing this method with more conventional methods like majority voting, and majority voting with confidence. The performance of the proposed method on NIST BSSR-1 dataset and FERET face dataset shows the efficacy of our approach measured by the accuracy improvements we are able to achieve by implicitly modelling the redundancy and diversity measures.
Biometric score fusion through discriminative training

In the multibiometric systems, various matcher/modality scores are fused together to provide better performance than the individual matcher scores. In  the authors have proposed a likelihood ratio test (LRT) based fusion technique for the biometric verification task that outperformed several other classifiers. They model the genuine and the imposter densities by the finite Gaussian mixture models (GMM, a generative model) whose parameters are estimated using the maximum likelihood (ML) criteria. Lately, the discriminative training methods and models have been shown to provide additional accuracy gains over the generative models, in multiple applications such as the speech recognition, verification and text analytics[5, 7]. These gains are based on the fact that the discriminative models are able to partially compensate for the unavoidable mismatch, which is always present between the specified statistical model (GMM in this case) and the true distribution of the data which is unknown. In this paper, we propose to use a discriminative method to estimate the GMM density parameters using the maximum accept and reject (MARS) criteria[8]. The test results using the proposed method on the NIST-BSSRI multimodal dataset indicate improved verification performance over a very competitive maximum likelihood (ML) trained system proposed in .
Enhancing security and privacy in biometrics-based authentication systems

Because biometrics-based authentication offers several advantages over other authentication methods, there has been a significant surge in the use of biometrics for user authentication in recent years. It is important that such biometrics-based authentication systems be designed to withstand attacks when employed in security-critical applications, especially in unattended remote applications such as e-commerce. In this paper we outline the inherent strengths of biometrics-based authentication, identify the weak links in systems employing biometrics-based authentication, and present new solutions for eliminating some of these weak links. Although, for illustration purposes, fingerprint authentication is used throughout, our analysis extends to other biometrics-based methods.
Impact of singular point detection on fingerprint matching performance

A majority of the minutiae based fingerprint verification algorithms rely on explicit or implicit alignment of the minutiae points for matching the two prints. With no prior knowledge about point correspondences, this becomes a combinatorial problem. Global features of the fingerprints such as the core and delta points represent intrinsic points of reference that can be used to align the two prints and reduce the computational complexity of the matcher. However, automatic extraction of singular points is usually error prone and is therefore not used by existing matchers. But, a systematic study of the impact on matching performance when core/delta points are available has not been done to date. In this paper, we explore the effects of the availability of reliable core and delta points on speed and accuracy of a matching algorithm. Towards this end, we present significant improvements to core and delta point detection algorithm based on complex filtering principles originally proposed by Nilsson et al., (2005). We also present a modified graph based matching algorithm that can run in O(n) time when the reference points are available. We analyse the resulting improvement in computational complexity and present experimental evaluation over FVC2002 database. We show that there is upto 43% improvement (70.2 ms to 39.8 ms) in average verification time and almost no loss in accuracy when reliable core and delta points are used.
Hierarchical partitioned least squares filter-bank for fingerprint enhancement

It is desirable to enhance the fingerprint image for achieving good fingerprint matching performance. In our earlier work, we have proposed a method for learning a set of partitioned least-squares filters from a given set of images and ground truth pairs. In this paper, we propose a refined approach to learn a class of hierarchical filter banks to extend our enhancement technique. The new technique is shown to obtain much better results particularly when the input image is not easily restored using our earlier technique. We evaluate the performance of our new approach to fingerprint enhancement filter design by assessing the effect of enhancement on performance of (i) fingerprint feature extraction and (ii) fingerprint matching.
Detecting dynamic behavior in compressed fingerprint videos: distortion

Distortions in fingerprint images arising from the elasticity of finger skin and the pressure and movement of fingers during image capture lead to great difficulties in establishing a match between multiple images acquired from a single finger. In a single fingerprint image depicting a finger at some given instant of time, it is difficult to get any distortion information. Further, static two-dimensional or three-dimensional (electronic) copies of fingerprints can be fabricated and used to spoof remote biometric security systems since the input required by the systems is not a function of time. This paper addresses these issues, by proposing the novel use of fingerprint video sequences to investigate and exploit dynamic behaviors manifested by fingers over time during image acquisition. In particular, we present a novel approach to detect and estimate distortion occurring in fingerprint video streams. Our approach directly works on MPEG-{1, 2} encoded fingerprint video bitstreams to estimate interfield flow without decompression, and uses flow characteristics to investigate temporal behaviour of the fingerprints. The joint temporal and motion analysis leads to a novel technique to detect and characterize distortion reliably. The proposed method has been tested on the NIST 24 database and the results are very promising.
Fingerprint Representation Using Localized Texture Features

Fingerprint representations can be broadly divided into three categories: image level, texture features and minutiae features. Both image based and texture based representations require accurate alignment before comparison. This presents a problem since accurate registration of fingerprints is challenging. On the other hand, minutiae based matchers are invariant to changes in orientation and position, but completely ignore the rich visual content in the image. In this paper, we present a localized texture based representation scheme that relies solely on visual content for identification and at the same time does not require absolute alignment. We outline techniques to efficiently compute these features and also propose an algorithm to perform identification based on these features. Our experimental evaluations over database of several sizes show that the proposed features are both accurate and scalable
Generating Cancelable Fingerprint Templates

Biometrics-based authentication systems offer obvious usability advantages over traditional password and token-based authentication schemes. However, biometrics raises several privacy concerns. A biometric is permanently associated with a user and cannot be changed. Hence, if a biometric identifier is compromised, it is lost forever and possibly for every application where the biometric is used. Moreover, if the same biometric is used in multiple applications, a user can potentially be tracked from one application to the next by cross-matching biometric databases. In this paper, we demonstrate several methods to generate multiple cancelable identifiers from fingerprint images to overcome these problems. In essence, a user can be given as many biometric identifiers as needed by issuing a new transformation "key". The identifiers can be cancelled and replaced when compromised. We empirically compare the performance of several algorithms such as Cartesian, polar, and surface folding transformations of the minutiae positions. It is demonstrated through multiple experiments that we can achieve revocability and prevent cross-matching of biometric databases. It is also shown that the transforms are noninvertible by demonstrating that it is computationally as hard to recover the original biometric identifier from a transformed version as by randomly guessing. Based on these empirical results and a theoretical analysis we conclude that feature-level cancelable biometric construction is practicable in large biometric deployments
Outlier faces detector via efficient cohesive subgraph identification

A personal or enterprise collection of a large set of face images may contain many types of tags used for querying the collection. Often the tags have many irrelevant content that may not reflect the image content in terms of the facial characteristics. In this paper, we propose a data curation method to filter out the irrelevant face images using a face recognition based subgraph identification. Results on retrievals from the Internet using popular celebrities show the efficacy of our approach after we cleanse the images collection retrieved and applying our algorithm to the collection.