nils-herrmann/clean_scirepeval huggingface dataset:
A high quality finger vascular pattern dataset collected using a custom designed capturing device

The number of finger vascular pattern datasets available for the research community is scarce, therefore a new finger vascular pattern dataset containing 1440 images is prsented. This dataset is unique in its kind as the images are of high resolution and have a known pixel density. Furthermore this is the first dataset which contains the age, gender and handedness of the participating volunteers as meta data. The images have been captured using a custom designed capturing device. The various aspects of designing this capturing device are addressed in this paper as well. To confirm whether this new dataset is in fact an important contribution some performance figures in terms of EER of several published state-of-the-art algorithms using this new dataset and an existing dataset from the Peking University are presented. Using this new dataset EERs down to 0.4% have been achieved.
Adaptive interpolation of discrete-time signals that can be modeled as autoregressive processes

This paper presents an adaptive algorithm for the restoration of lost sample values in discrete-time signals that can locally be described by means of autoregressive processes. The only restrictions are that the positions of the unknown samples should be known and that they should be embedded in a sufficiently large neighborhood of known samples. The estimates of the unknown samples are obtained by minimizing the sum of squares of the residual errors that involve estimates of the autoregressive parameters. A statistical analysis shows that, for a burst of lost samples, the expected quadratic interpolation error per sample converges to the signal variance when the burst length tends to infinity. The method is in fact the first step of an iterative algorithm, in which in each iteration step the current estimates of the missing samples are used to compute the new estimates. Furthermore, the feasibility of implementation in hardware for real-time use is established. The method has been tested on artificially generated auto-regressive processes as well as on digitized music and speech signals.
A Real Helper Data Scheme

The helper data scheme utilizes a secret key to protect biometric templates. The current helper data scheme requires binary feature representations that introduce quantization error and thus reduce the capacity of biometric channels. For spectral-minutiae based fingerprint recognition systems, Shannon theory proves that the current helper data scheme cannot have more than 6 bits. A 6-bit secret key is too short to secure the storage of biometric templates. Therefore, we propose a new helper data scheme without quantization. A basic realization is to convert the real-valued feature vector into a phase vector. By applying the spectral minutiae method in the FVC2000-DB2 fingerprint database, our new helper data scheme together with repetition codes and BCH codes allows at least 76 secret bits.
Likelihood-ratio-based biometric verification

The paper presents results on optimal similarity measures for biometric verification based on fixed-length feature vectors. First, we show that the verification of a single user is equivalent to the detection problem, which implies that, for single-user verification, the likelihood ratio is optimal. Second, we show that, under some general conditions, decisions based on posterior probabilities and likelihood ratios are equivalent and result in the same receiver operating curve. However, in a multi-user situation, these two methods lead to different average error rates. As a third result, we prove theoretically that, for multi-user verification, the use of the likelihood ratio is optimal in terms of average error rates. The superiority of this method is illustrated by experiments in fingerprint verification. It is shown that error rates below 10/sup -3/ can be achieved when using multiple fingerprints for template construction.
A Bayesian model for predicting face recognition performance using image quality

Quality of a pair of facial images is a strong indicator of the uncertainty in decision about identity based on that image pair. In this paper, we describe a Bayesian approach to model the relation between image quality (like pose, illumination, noise, sharpness, etc) and corresponding face recognition performance. Experiment results based on the MultiPIE data set show that our model can accurately aggregate verification samples into groups for which the verification performance varies fairly consistently. Our model does not require similarity scores and can predict face recognition performance using only image quality information. Such a model has many applications. As an illustrative application, we show improved verification performance when the decision threshold automatically adapts according to the quality of facial images.
Bit Rates in Audio Source Coding

The goal is to introduce and solve the audio coding optimization problem. Psychoacoustic results such as masking and excitation pattern models are combined with results from rate distortion theory to formulate the audio coding optimization problem. The solution of the audio optimization problem is a masked error spectrum, prescribing how quantization noise must be distributed over the audio spectrum to obtain a minimal bit rate and an inaudible coding errors. This result cannot only be used to estimate performance bounds, but can also be directly applied in audio coding systems. Subband coding applications to magnetic recording and transmission are discussed in some detail. Performance bounds for this type of subband coding system are derived.
Robust 3D face recognition in the presence of realistic occlusions

Facial occlusions pose significant problems for automatic face recognition systems. In this work, we propose a novel occlusion-resistant three-dimensional (3D) facial identification system. We show that, under extreme occlusions due to hair, hands, and eyeglasses, typical 3D face recognition systems exhibit poor performance. In order to deal with occlusions, our proposed system employs occlusion-resistant registration, occlusion detection, and regional classifiers. A two-step registration module first detects the nose region on the curvedness-weighted convex shape index map, and then performs fine alignment using nose-based Iterative Closest Point (ICP) algorithm. Occluded areas are determined automatically via a generic face model. After non-facial parts introduced by occlusions are removed, a variant of Gappy Principal Component Analysis (Gappy PCA) is used to restore the full face from occlusion-free facial surfaces. Experimental results obtained on realistically occluded facial images from the Bosphorus 3D face database shows that, with the use of score-level fusion of regional Linear Discriminant Analysis (LDA) classifiers, the proposed method improves rank-1 identification accuracy significantly: from 76.12% to 94.23%.
Robust Biometric Score Fusion by Naive Likelihood Ratio via Receiver Operating Characteristics

This paper presents a novel method of fusing multiple biometrics on the matching score level. We estimate the likelihood ratios of the fused biometric scores, via individual receiver operating characteristics (ROC) which construct the Naive Bayes classifier. Using a limited number of operation points on the ROC, we are able to realize reliable and robust estimation of the Naive Bayes probability without explicit estimation of the genuine and impostor score distributions. Different from previous work, the method takes into consideration a particular characteristic of the matching score: its quantitative value is already an indication of the sample's likelihood of being genuine. This characteristic is integrated into the proposed method to improve the fusion performance while reducing the inherent algorithmic complexity. We demonstrate by experiments that the proposed method is reliable and robust, suitable for a wide range of matching score distributions in realistic data and public databases.
Likelihood-ratio-based verification in high-dimensional spaces.

The increase of the dimensionality of data sets often leads to problems during estimation, which are denoted as the curse of dimensionality. One of the problems of second-order statistics (SOS) estimation in high-dimensional data is that the resulting covariance matrices are not full rank, so their inversion, for example, needed in verification systems based on the likelihood ratio, is an ill-posed problem, known as the singularity problem. A classical solution to this problem is the projection of the data onto a lower dimensional subspace using principle component analysis (PCA) and it is assumed that any further estimation on this dimension-reduced data is free from the effects of the high dimensionality. Using theory on SOS estimation in high-dimensional spaces, we show that the solution with PCA is far from optimal in verification systems if the high dimensionality is the sole source of error. For moderate dimensionality, it is already outperformed by solutions based on euclidean distances and it breaks down completely if the dimensionality becomes very high. We propose a new method, the fixed-point eigenwise correction, which does not have these disadvantages and performs close to optimal.
Verification Under Increasing Dimensionality

Verification decisions are often based on second order statistics estimated from a set of samples. Ongoing growth of computational resources allows for considering more and more features, increasing the dimensionality of the samples. If the dimensionality is of the same order as the number of samples used in the estimation or even higher, then the accuracy of the estimate decreases significantly. In particular, the eigenvalues of the covariance matrix are estimated with a bias and the estimate of the eigenvectors differ considerably from the real eigenvectors. We show how a classical approach of verification in high dimensions is severely affected by these problems, and we show how bias correction methods can reduce these problems.
A Bootstrap Approach to Eigenvalue Correction

Eigenvalue analysis is an important aspect in many data modeling methods. Unfortunately, the eigenvalues of the sample covariance matrix (sample eigenvalues) are biased estimates of the eigenvalues of the covariance matrix of the data generating process (population eigenvalues). We present a new method based on bootstrapping to reduce the bias in the sample eigenvalues: the eigenvalue estimates are updated in several iterations, where in each iteration synthetic data is generated to determine how to update the population eigenvalue estimates. Comparison of the bootstrap eigenvalue correction with a state of the art correction method by Karoui shows that depending on the type of population eigenvalue distribution, sometimes the Karoui method performs better and sometimes our bootstrap method.
Model-based reconstruction for illumination variation in face images

We propose a novel method to correct for arbitrary illumination variation in the face images. The main purpose is to improve recognition results of face images taken under uncontrolled illumination conditions. We correct the illumination variation in the face images using a face shape model, which allows us to estimate the face shape in the face image. Using this face shape, we can reconstruct a face image under frontal illumination. These reconstructed images improve the results in face identification. We experimented both with face images acquired under different controlled illumination conditions in a laboratory and under uncontrolled illumination conditions.
Beyond the eye of the beholder: On a forensic descriptor of the eye region

The task of forensic facial experts is to assess the likelihood whether a suspect is depicted on crime scene images. They typically (a) use morphological analysis when comparing parts of the facial region, and (b) combine this partial evidence into a final judgment. Facial parts can be considered as soft biometric modalities and in recent years have been studied in the biometric community. In this paper we focus on the region around the eye from a forensic perspective by applying the FISWG feature list of the eye modality. We compare existing work from the soft biometric perspective based on a texture descriptor with our approach.
Maximum Key Size and Classification Performance of Fuzzy Commitment for Gaussian Modeled Biometric Sources

Template protection techniques are used within biometric systems in order to protect the stored biometric template against privacy and security threats. A great portion of template protection techniques are based on extracting a key from, or binding a key to the binary vector derived from the biometric sample. The size of the key plays an important role, as the achieved privacy and security mainly depend on the entropy of the key. In the literature, it can be observed that there is a large variation on the reported key lengths at similar classification performance of the same template protection system, even when based on the same biometric modality and database. In this work, we determine the analytical relationship between the classification performance of the fuzzy commitment scheme and the theoretical maximum key size given as input a Gaussian biometric source. We show the effect of the system parameters such as the biometric source capacity, the number of feature components, the number of enrolment and verification samples, and the target performance on the maximum key size. Furthermore, we provide an analysis of the effect of feature interdependencies on the estimated maximum key size and classification performance. Both the theoretical analysis, as well as an experimental evaluation using the MCYT fingerprint database showed that feature interdependencies have a large impact on performance and key size estimates. This property can explain the large deviation in reported key sizes in literature.
Discriminating Power of FISWG Characteristic Descriptors Under Different Forensic Use Cases

FISWG characteristic descriptors are facial features that can be used for evidence evaluation during forensic case work. In this paper we investigate the discriminating power of a biometric system that uses these characteristic descriptors as features under different forensic use cases. We show that in every forensic use case we can find characteristic descriptors that exhibit moderate to low discriminating power. In all but one use cases, a commercial face recognition system outperforms the characteristic descriptors. However, in low resolution surveillance camera images, some (combination of) characteristic descriptors yield better results than commercial systems.
Identification Performance of Evidential Value Estimation for Fingermarks

Law enforcement agencies around the world use biometrics and fingerprints to solve and fight crime. Forensic experts are needed to record fingermarks at crime scenes and to ensure those captured are of evidential value. This process needs to be automated and streamlined as much as possible to improve efficiency and reduce workload. It has previously been demonstrated that is possible to estimate a fingermark's evidential value automatically for image captures taken with a mobile phone or other devices, such as a scanner or a high-quality camera. Here we study the relationship between a fingermark being of evidential value and its correct and certain identification and if it is possible to achieve identification despite the mark not having sufficient evidential value. Subsequently, we also investigate the influence the capture device used makes and if a mobile phone is a considerable option. Our results show that automatic identification is possible for 126 of the 1,428 fingermarks captured by a mobile phone, of which 116 were marked as having evidential value by experts and 123 by an automated algorithm.
Performances of the likelihood-ratio classifier based on different data modelings

The classical likelihood ratio classifier easily collapses in many biometric applications especially with independent training-test subjects. The reason lies in the inaccurate estimation of the underlying user-specific feature density. Firstly, the feature density estimation suffers from insufficient number of user-specific samples during the enrollment phase. Even if more enrollment samples are available, it is most likely that they are not reliable enough. Furthermore, it may happen that enrolled samples do not obey the Gaussian density model. Therefore, it is crucial to properly estimate the underlying user-specific feature density in the above situations. In this paper, we give an overview of several data modeling methods. Furthermore, we propose a discretized density based data model. Experimental results on FRGC face data set has shown reasonably good performance with our proposed model.
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.
A concatenated coding scheme for biometric template protection

Cryptography may mitigate the privacy problem in biometric recognition systems. However, cryptography technologies lack error-tolerance and biometric samples cannot be reproduced exactly, rising the robustness problem. The biometric template protection system needs a good feature extraction algorithm to be a good classifier. But, an even effective feature extractor can give a very low-quality biometric channel (i.e. high Bit Error Rate (BER)). Using the Spectral Minutiae method to identify fingerprints is one of the examples, which gives a BER of 40 ~ 50% to most of the matching channels. Therefore, we propose a concatenated coding scheme based on erasure codes to achieve a robust and secure biometric recognition system. The key idea is to transmit more packets than needed for decoding and allow the erasure-encoded packet suffering high BER to be discarded. The erasure decoder can reconstruct the secret key by collecting enough surviving packets. By applying the spectral minutiae method in the FVC2000-DB2 fingerprint database, the unprotected system achieves an EER of 3.7% and our proposed coding scheme reaches an EER of 4.6% with a 798-bit secret key.
The spectral relevance of glottal-pulse parameters

The paper analyses how variations of the parameters of the Liljencrants-Fant (1985) model of glottal flow influence the speech spectrum, in order to determine the spectral relevance of these parameters. The effects of small parameter variations are described analytically. This analysis also gives an indication to what extent the LF parameters can be estimated reliably from the speech spectrum. The effects of larger parameter variations are discussed with the help of figures. Results are presented for a number of sets of estimated glottal-pulse parameters that were taken from the literature. The main conclusion is that the LF model, which, given the fundamental period, is a three-parameter model, actually operates as a one- or a two-parameter model.
Hybrid fusion for biometrics: Combining score-level and decision-level fusion

A general framework of fusion at decision level, which works on ROCs instead of matching scores, is investigated. Under this framework, we further propose a hybrid fusion method, which combines the score-level and decision-level fusions, taking advantage of both fusion modes. The hybrid fusion adaptively tunes itself between the two levels of fusion, and improves the final performance over the original two levels. The proposed hybrid fusion is simple and effective for combining different biometrics.
Binary Representations of Fingerprint Spectral Minutiae Features

A fixed-length binary representation of a fingerprint has the advantages of a fast operation and a small template storage. For many biometric template protection schemes, a binary string is also required as input. The spectral minutiae representation is a method to represent a minutiae set as a fixed-length real-valued feature vector. In order to be able to apply the spectral minutiae representation with a template protection scheme, we introduce two novel methods to quantize the spectral minutiae features into binary strings: Spectral Bits and Phase Bits. The experiments on the FVC2002 database show that the binary representations can even outperformed the spectral minutiae real-valued features.
Impact of eye detection error on face recognition performance

The locations of the eyes are the most commonly used features to perform face normalisation (i.e. alignment of facial features), which is an essential preprocessing stage of many face recognition systems. In this study, the authors study the sensitivity of open source implementations of five face recognition algorithms to misalignment caused by eye localisation errors. They investigate the ambiguity in the location of the eyes by comparing the difference between two independent manual eye annotations. They also study the error characteristics of automatic eye detectors present in two commercial face recognition systems. Furthermore, they explore the impact of using different eye detectors for training/enrolment and query phases of a face recognition system. These experiments provide an insight into the influence of eye localisation errors on the performance of face recognition systems and recommend a strategy for the design of training and test sets of a face recognition algorithm.
Designing a Low-Resolution Face Recognition System for Long-Range Surveillance

Most face recognition systems deal well with high-resolution facial images, but perform much worse on low-resolution facial images. In low-resolution face recognition, there is a specific but realistic surveillance scenario: a surveillance camera monitoring a large area. In this scenario, usually the gallery images are of high-resolution and the probe images are of various low-resolutions depending on the distances between the subject and the camera. In this paper, we design a low-resolution face recognition system for this scenario. We use a state-of-the-art mixed-resolution classifier to deal with the resolution mismatch between the gallery and probe images. We also set up experiments to explore the best training configuration for probe images of various resolutions. Our experimental results show that one classifier which is trained on images of various resolutions covering the whole range has promising results in the long-range surveillance scenario. This system has at least as good performance as combining multiple face recognition systems that are optimised for different resolutions.