nils-herrmann/clean_scirepeval huggingface dataset:
ForenFace: a unique annotated forensic facial image dataset and toolset

Few facial image datasets are suitable for forensic research. In this study, the authors present ForenFace, a facial image and video dataset. It contains video sequences and extracted images of 97 subjects recorded with six different surveillance camera of various types. Moreover, it also contains high-resolution images and 3D scans. The novelty of this dataset lies in two aspects: (i) a subset of 435 images (87 subjects, five images per subject) has been manually annotated, yielding a very rich forensically relevant annotation of almost 19.000 facial parts, and (ii) making available a toolset to create, view, and extract the annotation. The authors present protocols and the result of a baseline experiment in which two commercial software packages and an annotated facial feature contained in this dataset are compared. The dataset, the annotation and tools are available under a usage license.
Spectral minutiae representations of fingerprints enhanced by quality data

Many fingerprint recognition systems are based on minutiae matching. However, the recognition accuracy of minutiae-based matching algorithms is highly dependent on the fingerprint minutiae quality. Therefore, in this paper, we introduce a quality integrated spectral minutiae algorithm, in which the minutiae quality information is incorporated to enhance the performance of the spectral minutiae fingerprint recognition system. In our algorithm, two types of quality data are used. The first one is the minutiae reliability, expressing the probability that a given point is indeed a minutia; the second one is the minutiae location accuracy, quantifying the error on the minutiae location. We integrate these two types of quality information into the spectral minutiae representation algorithm and achieve a decrease in the Equal Error Rate of over 20% in the experiment.
Multi-Bits Biometric String Generation based on the Likelihood Ratio

Preserving the privacy of biometric information stored in biometric systems is becoming a key issue. An important element in privacy protecting biometric systems is the quantizer which transforms a normal biometric template into a binary string. In this paper, we present a user-specific quantization method based on a likelihood ratio approach (LQ). The bits generated from every feature are concatenated to form a fixed length binary string that can be hashed to protect its privacy. Experiments are carried out on both fingerprint data (FVC2000) and face data (FRGC). Results show that our proposed quantization method achieves a reasonably good performance in terms of FAR/FRR (when FAR is 10 4, the corresponding FRR are 16.7% and 5.77% for FVC2000 and FRGC, respectively).
A 3-layer coding scheme for biometry template protection based on spectral minutiae

Spectral Minutiae (SM) representation enables the combination of minutiae-based fingerprint recognition systems with template protection schemes based on fuzzy commitment, but it requires error-correcting codes that can handle high bit error rates (i.e. above 40%). In this paper, we propose a 3-Layer coding scheme based on erasure codes for the SM-based biometric recognition system. Our approach is inspired by the fact that the Packet Error Rate (PER) is proportional to the Bit Error Rate (BER). Each packet is encoded by an Error Detection Code (EDC) and an Error Correction Code (ECC). The packet can only survive if it successfully passes the ECC and EDC decoder. With the erasure code, the system can reconstruct the secret key by only using the survived packets. By applying SM to the FVC2000-DB2 fingerprint database, the unprotected system achieves an EER of around 6% while our proposed coding scheme reaches an EER of approximately 6.5% with a 1032-bit secret key.
On the use of spectral minutiae in high-resolution palmprint recognition

The spectral minutiae representation has been proposed as a novel method to minutiae-based fingerprint recognition, which can handle minutiae translation and rotation and improve matching speed. As high-resolution palmprint recognition is also mainly based on minutiae sets, we apply spectral minutiae representation to palmprints and implement spectral minutiae based matching. We optimize key parameters for the method by experimental study on the characteristics of spectral minutiae using both fingerprints and palmprints. However, experimental results show that spectral minutiae representation has much worse performance for palmprints than that for fingerprints. EER 15.89% and 14.2% are achieved on the public high-resolution palmprint database THUPALMLAB using location-based spectral minutiae representation (SML) and the complex spectral minutiae representation (SMC) respectively while 5.1% and 3.05% on FVC2002 DB2A fingerprint database. Based on statistical analysis, we find the worse performance for palmprints mainly due to larger non-linear distortion and much larger number of minutiae.
Biometric binary string generation with detection rate optimized bit allocation

Extracting binary strings from real-valued templates has been a fundamental issue in many biometric template protection systems. In this paper, we present an optimal bit allocation method (OBA). By means of it, a binary string at a pre-defined length with maximized overall detection rate is generated. Experiments with the binary strings and a Hamming distance classifier on FRGC and FERET databases show promising performance in terms of FAR and FRR.
Interpolating autoregressive processes: a bound on the restoration error

An upper bound is obtained for the restoration error variance of a sample restoration method for autoregressive processes that was presented by A.J.E.M. Janssen et al. (ibid., vol.ASSP-34, p.317-30, Apr. 1986). The upper bound derived is lower if the autoregressive process has poles close to the unit circle of the complex plane. This situation corresponds to a peaky signal spectrum. The bound is valid for the case in which one sample is unknown in a realization of an autoregressive process of arbitrary finite order.
Illumination Normalization Based on Simplified Local Binary Patterns for A Face Verification System

Illumination normalization is a very important step in face recognition. In this paper we propose a simple implementation of local binary patterns, which effectively reduces the variability caused by illumination changes. In combination with a likelihood ratio classifier, this illumination normalization method achieves very good recognition performance, with respect to both discrimination and generalization. A user verification system using this method has been successfully implemented on a mobile platform.
Subband coding of digital audio signals without loss of quality

A subband coding system for high quality digital audio signals is described. To achieve low bit rates at a high quality level, it exploits the simultaneous masking effect of the human ear. It is shown how this effect can be used in an adaptive bit-allocation scheme. The proposed approach has been applied in two coding systems, a complex system in which signal is split into 26 subbands, each approximately one third of an octave wide, and a simpler 20-band system. Both systems have been designed for coding stereophonic 16-bit compact disk signals with a sampling frequency of 44.1 kHz. With the 26-band system high-quality results can be obtained at bit rates of 220 kb/s. With the 20-band system, similar results can be obtained at bit rates of 360 kb/s.<<ETX>>
Spectral Representations of Fingerprint Minutiae Subsets

The investigation of the privacy protection of biometric templates gains more and more attention. The spectral minutiae representation is a novel method to represent a minutiae set as a fixed-length feature vector, which is invariant to translation, and in which rotation and scaling become translations, so that they can be easily compensated for. These characteristics enable the combination of fingerprint recognition systems with template protection schemes that require as an input a fixed-length feature vector. However, the limited overlap of a fingerprint pair can reduce the performance of the spectral minutiae representation algorithm. Therefore, in this paper, we introduce the spectral representations of fingerprint minutiae subsets to cope with the limited overlap problem. In the experiment, we improve the recognition performance from 0.32% to 0.12% in equal error rate after applying the spectral representations of minutiae subsets algorithm.
Preventing the Decodability Attack Based Cross-Matching in a Fuzzy Commitment Scheme

Template protection techniques are used within biometric systems in order to safeguard the privacy of the system's subjects. This protection also includes unlinkability, i.e., preventing cross-matching between two or more reference templates from the same subject across different applications. In the literature, the template protection techniques based on fuzzy commitment, also known as the code-offset construction, have recently been investigated. Recent work presented the decodability attack vulnerability facilitating cross-matching based on the protected templates and its theoretical analysis. First, we extend the theoretical analysis and include the comparison between the system and cross-matching performance. We validate the presented analysis using real biometric data from the MCYT fingerprint database. Second, we show that applying a random bit-permutation process secures the fuzzy commitment scheme from cross-matching based on the decodability attack.
Local Absolute Binary Patterns as Image Preprocessing for Grip-Pattern Recognition in Smart Gun

In a biometric verification system of a smart gun, the rightful user is recognized based on his hand-pressure pattern. The main factor which affects the verification performance of this system is the variation between the probe image and the gallery image of a subject, in particular when the probe and the gallery images have been recorded with a few weeks in between. One of the major variations is in the pressure distribution of images. In this work, we propose a novel preprocessing technique, Local Absolute Binary Patterns, prior to grip-pattern classification. With respect to a certain pixel in an image, Local Absolute Binary Patterns processing quantifies how its neighboring pixels are fluctuating. It will be shown that this technique can both reduce the variation of pressure distribution, and extract information of the hand shape in the image. Therefore, a significant improvement of the verification result has been achieved.
Extraction of vocal-tract system characteristics from speech signals

We propose methods to track natural variations in the characteristics of the vocal-tract system from speech signals. We are especially interested in the cases where these characteristics vary over time, as happens in dynamic sounds such as consonant-vowel transitions. We show that the selection of appropriate analysis segments is crucial in these methods, and we propose a selection based on estimated instants of significant excitation. These instants are obtained by a method based on the average group-delay property of minimum-phase signals. In voiced speech, they correspond to the instants of glottal closure. The vocal-tract system is characterized by its formant parameters, which are extracted from the analysis segments. Because the segments are always at the same relative position in each pitch period, in voiced speech the extracted formants are consistent across successive pitch periods. We demonstrate the results of the analysis for several difficult cases of speech signals.
Occlusion-robust 3D face recognition using restoration and local classifiers

Occlusions complicate the process of identifying individuals using their 3D facial scans. We propose a 3D face recognition system that automatically removes occlusion artifacts and identifies the facial image using regional classifiers. Automatic localization of occluded areas is handled by using a generic face model. Restoration of missing information after occlusion removal is performed by the application of an improved version of Gappy Principal Component Analysis (GPCA), which we call partial Gappy PCA (pGPCA). After the removal of noisy data introduced by realistic occlusions, occlusion-free faces are represented by local regions. Local classifiers operating on these local regions are then fused to achieve occlusion-robust identification performance. Our experimental results obtained on realistically occluded facial images from the Bosphorus 3D face database illustrate that our occlusion compensation scheme drastically improves the recognition accuracy from 78.05% to 94.20%.
Complex spectral minutiae representation for fingerprint recognition

The spectral minutiae representation is designed for combining fingerprint recognition with template protection. This puts several constraints to the fingerprint recognition system: first, no relative alignment of two fingerprints is allowed due to the encrypted storage; second, a fixed-length feature vector is required as input of template protection schemes. The spectral minutiae representation represents a minutiae set as a fixed-length feature vector, which is invariant to translation, rotation and scaling. These characteristics enable the combination of fingerprint recognition systems with template protection schemes and allow for fast minutiae-based matching as well. In this paper, we introduce the complex spectral minutiae representation (SMC): a spectral representation of a minitiae set, as the location-based and the orientation-based spectral minutiae representations (SML and SMO), but it encodes minutiae orientations differently. SMC improves the recognition accuracy, expressed in term of the Equal Error Rate, about 2–4 times compared with SML and SMO. In addition, the paper presents two feature reduction algorithms: the Column-PCA and the Line-DFT feature reductions, which achieve a template size reduction around 90% and results in a 10–15 times higher matching speed (with 125,000 comparisons per second).
Quantifying privacy and security of biometric fuzzy commitment

Fuzzy commitment is an efficient template protection algorithm that can improve security and safeguard privacy of biometrics. Existing theoretical security analysis has proved that although privacy leakage is unavoidable, perfect security from information-theoretical points of view is possible when bits extracted from biometric features are uniformly and independently distributed. Unfortunately, this strict condition is difficult to fulfill in practice. In many applications, dependency of binary features is ignored and security is thus suspected to be highly overestimated. This paper gives a comprehensive analysis on security and privacy of fuzzy commitment regarding empirical evaluation. The criteria representing requirements in practical applications are investigated and measured quantitatively in an existing protection system for 3D face recognition. The evaluation results show that a very significant reduction of security and enlargement of privacy leakage occur due to the dependency of biometric features. This work shows that in practice, one has to explicitly measure the security and privacy instead of trusting results under non-realistic assumptions.
Spectral Minutiae Representations for Fingerprint Recognition

The spectral minutiae representation introduced in this paper is a novel method to represent a minutiae set as a fixed-length feature vector, which is invariant to translation, and in which rotation becomes translation that can be easily compensated for. These characteristics enable the combination of fingerprint recognition systems with a template protection scheme that requires a fixed-length feature vector as input. In this paper, we will first introduce the spectral minutiae representation scheme. Then we will present several biometric fusion approaches to improve the biometric system performance by combining multiple sources of biometric information. The algorithms are evaluated on the FVC2000-DB2 database and showed promising results.
Grid-Based Likelihood Ratio Classifiers for the Comparison of Facial Marks

Facial marks have been studied before, either as a complement to face recognition systems or for their suitability as a single biometric modality. In this paper, we use a subset of the FRGCv2 data set (12307 images and 568 subjects) to study the properties of facial marks, their spatial patterns, and classifiers acting upon these patterns. We observe differences between age and ethnic groups in the number of facial marks. Also, facial marks tend to be clustered. We present six forensically relevant aspects with respect to the design and evaluation of classifiers. These aspects help to systematically study factors that influence performance characteristics (discriminating power and calibration loss) of these classifiers. Calibration loss is of particular forensic importance; it essentially measures how well the classifier output can be used as strength of evidence in a court of law. We use various facial mark grids to which the facial mark spatial patterns are assigned. We find that a classifier that utilizes the facial mark grid of a specific subject outperforms all other classifiers. We also observe that the calibration loss of such subject-based classifier indicates that small grid cell sizes should be avoided.
Decision Level Fusion of Fingerprint Minutiae Based Pseudonymous Identifiers

In a biometric template protected authentication system, a pseudonymous identifier is the part of a protected biometric template that can be compared directly against other pseudonymous identifiers. Each compared pair of pseudonymous identifiers results in a verification decision testing whether both attributes are derived from the same individual. Compared to an unprotected system, most existing biometric template protection methods cause to a certain extent, degradation in biometric performance. Therefore fusion is a promising method to enhance the biometric performance in template protected systems. Compared to feature level fusion and score level fusion, decision level fusion exhibits not only the least fusion complexity, but also the maximum interoperability across different biometric features, systems based on scores, and even individual algorithms. However, performance improvement via decision level fusion is not obvious. It is influenced by both the dependency and the performance gap among the conducted tests for fusion. We investigate in this paper several scenarios (multi-sample, multi-instance, multi- sensor, and multi-algorithm) when fusion is performed on binary decisions obtained from verification of fingerprint minutiae based pseudonymous identifiers. We demonstrate the influence on biometric performance from decision level fusion in different fusion scenarios on a multi-sensor fingerprint database.
A Fast Minutiae-Based Fingerprint Recognition System

The spectral minutiae representation is a method to represent a minutiae set as a fixed-length feature vector, which is invariant to translation, and in which rotation and scaling become translations, so that they can be easily compensated for. These characteristics enable the combination of fingerprint recognition systems with template protection schemes that require as an input a fixed-length feature vector. Based on the spectral minutiae features, this paper introduces two feature reduction algorithms: the Column Principal Component Analysis and the Line Discrete Fourier Transform feature reductions, which can efficiently compress the template size with a reduction rate of 94%. With reduced features, we can also achieve a fast minutiae-based matching algorithm. This paper presents the performance of the spectral minutiae fingerprint recognition system and shows a matching speed with 125 000 comparisons per second on a PC with Intel Pentium D processor 2.80 GHz and 1 GB of RAM. This fast operation renders our system suitable as a preselector for a large-scale fingerprint identification system, thus significantly reducing the time to perform matching, especially in systems operating at geographical level (e.g., police patrolling) or in complex critical environments (e.g., airports).
The Effect of Image Resolution on the Performance of a Face Recognition System

In this paper we investigate the effect of image resolution on the error rates of a face verification system. We do not restrict ourselves to the face recognition algorithm only, but we also consider the face registration. In our face recognition system, the face registration is done by finding landmarks in a face image and subsequent alignment based on these landmarks. To investigate the effect of image resolution we performed experiments where we varied the resolution. We investigate the effect of the resolution on the face recognition part, the registration part and the entire system. This research also confirms that accurate registration is of vital importance to the performance of the face recognition algorithm. The results of our face recognition system are optimal on face images with a resolution of 32 times 32 pixels
Manually annotated characteristic descriptors: Measurability and variability

In this paper we study the measurability and variability of manually annotated characteristic descriptors on a forensic relevant face dataset. Characteristic descriptors are facial features (landmarks, shapes, etc.) that can be used during forensic case work. With respect to measurability, we observe that a significant proportion cannot be determined in images representative of forensic case work. Landmarks, closed and open shapes, and other forensic facial features show mostly that the variability depends on the image quality. Up to 50% of all considered evidential values are either positively or negatively influenced by annotator variability. However, when considering images with the lowest quality, we found that more than 70% of the evidential value intervals in principle could yield the wrong conclusion.
Biometric Authentication for a Mobile Personal Device

Secure access is prerequisite for a mobile personal device (MPD) in a personal network (PN). An authentication method using biometrics, specifically face, is proposed in this paper. A fast face detection and registration method based on a Viola-Jones detector is implemented, and a face-authentication method based on subspace metrics is developed. Experiments show that the authentication method is effective with an equal error rate (EER) of 1.2%, despite its simplicity
Pseudo Identities Based on Fingerprint Characteristics

This paper presents the integrated project TURBINE which is funded under the EU 7th research framework programme. This research is a multi-disciplinary effort on privacy enhancing technology, combining innovative developments in cryptography and fingerprint recognition. The objective of this project is to provide a breakthrough in electronic authentication for various applications in the physical world and on the Internet. On the one hand it will provide secure identity verification thanks to fingerprint recognition. On the other hand it will reliably protect the biometric data through advanced cryptography technology. In concrete terms, it will provide the assurance that (i) the data used for the authentication, generated from the fingerprint, cannot be used to restore the original fingerprint sample, (ii) the individual will be able to create different "pseudo-identities" for different applications with the same fingerprint, whilst ensuring that these different identities (and hence the related personal data) cannot be linked to each other, and (iii) the individual is enabled to revoke an biometric identifier (pseudo-identity) for a given application in case it should not be used anymore.