nils-herrmann/clean_scirepeval huggingface dataset:
Statistical Pattern Recognition: A Review

The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learning theory have been receiving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.
An introduction to biometrics

Biometrics refers to the automatic identification (or verification) of an individual (or a claimed identity) by using certain physical or behavioral traits associated with the person. By using biometrics it is possible to establish an identity based on `who you are?, rather than by `what you possess? (e.g., an ID card) or `what you remember? (e.g., a password). Therefore, biometric systems use fingerprints, hand geometry, iris, retina, face, vasculature patterns, signature, gait, palmprint, or voiceprint to determine a person?s identity. The purpose of this tutorial is two-fold: (a) to introduce the fundamentals of biometric technology from a pattern recognition and signal processing perspective by discussing some of the prominent techniques used in the field; and (b) to convey the recent advances made in this field especially in the context of security, privacy and forensics. To this end, the design of a biometric system will be discussed from the viewpoint of four commonly used biometric modalities - fingerprint, face, hand, and iris. Various algorithms that have been developed for processing these modalities will be presented.  to protect the biometric templates of enrolled users will also be outlined. In particular, the possibility of performing biometric matching in the cryptographic domain will be discussed. The tutorial will also introduce concepts in biometric fusion (i.e., multibiometrics) in which multiple sources of biometric information are consolidated. Finally, there will be a discussion on some of the challenges encountered by biometric systems when operating in a real-world environment and some of the methods used to address these challenges.
CAD-Based Computer Vision: From CAD Models to Relational Graphs

The topic of model-building for 3-D objects is examined. Most 3-D object recognition systems construct models either manually or by training. Neither approach has been very satisfactory, particularly in designing object recognition systems which can handle a large number of objects. Recent interest in integrating mechanical CAD systems and vision systems has led to a third type of model building for vision: adaptation of preexisting CAD models of objects for recognition. If a solid model of an object to be recognized is already available in a manufacturing database, then it should be possible to infer automatically a model appropriate for vision tasks from the manufacturing model. Such a system has been developed. It uses 3-D object descriptions created on a commercial CAD system and expressed in both the industry-standard IGES form and a polyhedral approximation and performs geometric inferencing to obtain a relational graph representation of the object which can be stored in a database of models for object recognition. Relational graph models contain both view-independent information extracted from the IGES description and view-dependent information (patch areas) extracted from synthetic views of the object. It is argued that such a system is needed to efficiently create a large database (more than 100 objects) of 3-D models to evaluate matching strategies.
Image Retrieval in Forensics: Tattoo Image Database Application

In this article, we took an unsupervised approach in designing appropriate similarity measures to explicitly address the challenge arising from low-quality tattoo image matching. In the future, we plan to improve the matching algorithm by exploring both super- vised and semisupervised learning algorithms. Besides tattoos, other types of soft forensic evidence can be collected and managed in the form of images, such as shoe prints and gang graffiti images. Although Tattoo-ID focuses on tattoo image matching and retrieval, the underlying techniques developed in the Tattoo-ID system can be adopted to other forensic image databases.15 Other types of soft forensic image evidence might include shoeprints and gang graffiti images. In the future, we plan to extend the Tattoo-ID system to different application domains.
Robust data clustering

We address the problem of robust clustering by combining data partitions (forming a clustering ensemble) produced by multiple clusterings. We formulate robust clustering under an information-theoretical framework; mutual information is the underlying concept used in the definition of quantitative measures of agreement or consistency between data partitions. Robustness is assessed by variance of the cluster membership, based on bootstrapping. We propose and analyze a voting mechanism on pairwise associations of patterns for combining data partitions. We show that the proposed technique attempts to optimize the mutual information based criteria, although the optimality is not ensured in all situations. This evidence accumulation method is demonstrated by combining the well-known K-means algorithm to produce clustering ensembles. Experimental results show the ability of the technique to identify clusters with arbitrary shapes and sizes.
Webbiometrics: User Verification Via Web Interaction

We introduce a biometric trait based on the user behavior extracted from his interaction with a Web page. We propose the integration of this soft biometric trait in a conventional login Internet page to enhance the security of the system. We call this security layer WebBiometrics. This layer monitors the user mouse movements while he clicks his PIN code numbers. The proposed biometric method provides a non-intrusive soft behavioral biometric add-on to enhance on-line security. We describe the functionality of the system, the set of algorithms developed for the verification framework and preliminary experimental results. We also present quantitative measures of security enhancement offered by the introduction of this soft biometric compared to a PIN only based Web access.
Performance evaluation of fingerprint verification systems

This paper is concerned with the performance evaluation of fingerprint verification systems. After an initial classification of biometric testing initiatives, we explore both the theoretical and practical issues related to performance evaluation by presenting the outcome of the recent fingerprint verification competition (FVC2004). FVC2004 was organized by the authors of this work for the purpose of assessing the state-of-the-art in this challenging pattern recognition application and making available a new common benchmark for an unambiguous comparison of fingerprint-based biometric systems. FVC2004 is an independent, strongly supervised evaluation performed at the evaluators' site on evaluators' hardware. This allowed the test to be completely controlled and the computation times of different algorithms to be fairly compared. The experience and feedback received from previous, similar competitions (FVC2000 and FVC2002) allowed us to improve the organization and methodology of FVC2004 and to capture the attention of a significantly higher number of academic and commercial organizations (67 algorithms were submitted for FVC2004). A new, "light" competition category was included to estimate the loss of matching performance caused by imposing computational constraints. This paper discusses data collection and testing protocols, and includes a detailed analysis of the results. We introduce a simple but effective method for comparing algorithms at the score level, allowing us to isolate difficult cases (images) and to study error correlations and algorithm "fusion." The huge amount of information obtained, including a structured classification of the submitted algorithms on the basis of their features, makes it possible to better understand how current fingerprint recognition systems work and to delineate useful research directions for the future.
Face Image Reconstruction from Deep Templates

State-of-the-art face recognition systems are based on deep (convolutional) neural networks. Therefore, it is imperative to determine to what extent face templates derived from deep networks can be inverted to obtain the original face image. In this paper, we discuss the vulnerabilities of a face recognition system based on deep templates, extracted by deep networks under image reconstruction attack. We propose a de-convolutional neural network (D-CNN) to reconstruct images of faces from their deep templates. In our experiments, we did not assume any knowledge about the target subject nor the deep network. To train the D-CNN reconstruction models, we augmented existing face datasets with a large collection of images synthesized using a face generator. The proposed reconstruction method was evaluated using type-I (comparing the reconstructed images against the original face images used to generate the deep template) and type-II (comparing the reconstructed images against a different face image of the same subject) attacks. We conducted a three-trial attack for each target face image using three face images reconstructed from three different D-CNNs. Each D-CNN was trained on a different dataset (VGG-Face, CASIA-Webface, or Multi-PIE). The type-I attack achieved a true accept rate (TAR) of 85.48% at a false accept rate (FAR) of 0.1% on the LFW dataset. The corresponding TAR for the type-II attack is 14.71%. Our experimental results demonstrate the need to secure deep templates in face recognition systems.
On-Line Fingerprint Verification

Fingerprint verification is one of the most reliable personal identification methods. However, manual fingerprint verification is incapable of meeting today's increasing performance requirements. An automatic fingerprint identification system (AFIS) is needed. This paper describes the design and implementation of an online fingerprint verification system which operates in two stages: minutia extraction and minutia matching. An improved version of the minutia extraction algorithm proposed by Ratha et al. (1995), which is much faster and more reliable, is implemented for extracting features from an input fingerprint image captured with an online inkless scanner. For minutia matching, an alignment-based elastic matching algorithm has been developed. This algorithm is capable of finding the correspondences between minutiae in the input image and the stored template without resorting to exhaustive search and has the ability of adaptively compensating for the nonlinear deformations and inexact pose transformations between fingerprints. The system has been tested on two sets of fingerprint images captured with inkless scanners. The verification accuracy is found to be acceptable. Typically, a complete fingerprint verification procedure takes, on an average, about eight seconds on a SPARC 20 workstation. These experimental results show that our system meets the response time requirements of online verification with high accuracy.
Template Adaptation based Fingerprint Verification

This paper proposes a minutiae-based template adaptation algorithm which can be applied after the fingerprint authentication process. The algorithm updates a template by using a query fingerprint, which is successfully verified by the fingerprint matcher as a high quality genuine input. This algorithm generates an updated minutiae set by using not only the minutiae but also local fingerprint quality information and utilizes a successive Bayesian estimation to evaluate the credibility of minutiae and their types. The proposed algorithm updates fingerprint minutiae information in the template as well as appends new minutiae from the query fingerprint. Preliminary experiments show an average 32.7% EER reduction and an even higher matching accuracy improvement at low false accept rates
Unconstrained face detection: State of the art baseline and challenges

A large scale study of the accuracy and efficiency of face detection algorithms on unconstrained face imagery is presented. Nine different face detection algorithms are studied, which are acquired through either government rights, open source, or commercial licensing. The primary data set utilized for analysis is the IAPRA Janus Benchmark A (IJB-A), a recently released unconstrained face detection and recognition dataset which, at the time of this study, contained 67,183 manually localized faces in 5,712 images and 20,408 video frames. The goal of the study is to determine the state of the art in face detection with respect to unconstrained imagery which is motivated by the saturation of recognition accuracies on seminal unconstrained face recognition datasets which are filtered to only contain faces detectable by a commodity face detection algorithm. The most notable finding from this study is that top performing detectors still fail to detect the vast majority of faces with extreme pose, partial occlusion, and/or poor illumination. In total, over 20% of faces fail to be detected by all nine detectors studied. The speed of the detectors was generally correlated with accuracy: faster detectors were less accurate than their slower counterparts. Finally, key considerations and guidance is provided for performing face detection evaluations. All software using these methods to conduct the evaluations and plot the accuracies are made available in the open source.
Latent fingerprint indexing: Fusion of level 1 and level 2 features

Fingerprints have been widely used as a biometric trait for person recognition. Due to the wide acceptance and deployment of fingerprint matching systems, there is a steady increase in the size of fingerprint databases in law enforcement and national ID agencies. Thus, it is of great interest to develop methods that, for a given query fingerprint (rolled or latent), can efficiently filter out a large portion of the reference or background database based on a coarse matching (or indexing) strategy. In this work, we propose an indexing technique, primarily for latents, that combines multiple level 1 and level 2 features to filter out a large portion of the background database while maintaining the latent matching accuracy. Our approach consists of combining minutiae, singular points, orientation field and frequency information. Experimental results carried out on 258 latents in NIST SD27 against a large background database (267K rolled prints) show that the proposed approach outperforms state-of-the-art fingerprint indexing techniques reported in the literature. At a penetration rate of 20%, our approach can reach a hit rate of 90.3%, with a five-fold reduction in the latent search (indexing + matching) time, while maintaining the latent matching accuracy.
Tattoo based identification: Sketch to image matching

Tattoos on human body provide important clue to the identity of a suspect. While a tattoo is not an unique identifier, it narrows down the list of identities for the suspect. For these reasons, law enforcement agencies have been collecting tattoo images of the suspects at the time of booking. A few successful attempts have been made to design an automatic system to search a tattoo database to identify near-duplicate images of a query tattoo image. However, in many scenarios, the surveillance image of the crime scene is not available, so the query is in the form of a sketch of a tattoo (as opposed to an image of a tattoo) drawn based on the description provided by an eye-witness. In this paper, we extend the capability of tattoo image-to-image matching by proposing a method to match tattoo sketches to tattoo images using local invariant features. Specifically, tattoo shape is first extracted from both tattoo sketch and tattoo image using Canny edge detector. Local patterns are then extracted from tattoo shape as well as tattoo image (appearance) using SIFT A local feature based sparse representation classification scheme is then used for matching. Experimental results on matching 100 tattoo sketches against a gallery set with 10,100 tattoo images show that the proposed method achieves significant improvement (rank-200 accuracy of 57%) compared to a state-of-the-art tattoo image-to-image matching system (rank-200 accuracy of 19%).
Learning the human face concept in black and white images

Presents a learning approach for the face detection problem. The problem can be stated as follows: given an arbitrary black and white, still image, find the location and size of every human face it contains. Numerous applications of automatic face detection have attracted considerable interest in this problem, but no present face detection system is completely satisfactory from the point of view of detection rate, false alarm rate and detection time. We describe an inductive learning-based detection method that produces a maximally specific hypothesis consistent with the training data. Three different sets of features were considered for defining the concept of a human face. The performance achieved is as follows: 85% detection rate, a false alarm rate of 0.04% of the number of windows analyzed and 1 minute detection table for a 320/spl times/240 image on a Sun Ultrasparc 1.
A 1.2 GHz Alpha microprocessor with 44.8 GB/s chip pin bandwidth

A 4th-generation Alpha microprocessor running at 1.2 GHz delivers up to 44.8 GB/s chip pin bandwidth and dissipates 125 W at 1.5 V. It contains a 1.75 MB 2nd level write-back-cache, two memory controllers supporting 8 Rambus/sup TM/ channels running at 800 Mb/s, four 6.4 GB/s inter-processor communication ports, and a separate 10 port capable of 6.4 GB/s. The chip measures 21.1/spl times/18.8 mm/sup 2/, contains 130 M transistors and is fabricated in a 0.18 /spl mu/m bulk CMOS process with 7 levels of copper interconnect.
Representation and Recognition of Handwritten Digits Using Deformable Templates

We investigate the application of deformable templates to recognition of handprinted digits. Two characters are matched by deforming the contour of one to fit the edge strengths of the other, and a dissimilarity measure is derived from the amount of deformation needed, the goodness of fit of the edges, and the interior overlap between the deformed shapes. Classification using the minimum dissimilarity results in recognition rates up to 99.25 percent on a 2,000 character subset of NIST Special Database 1. Additional experiments on an independent test data were done to demonstrate the robustness of this method. Multidimensional scaling is also applied to the 2,000/spl times/2,000 proximity matrix, using the dissimilarity measure as a distance, to embed the patterns as points in low-dimensional spaces. A nearest neighbor classifier is applied to the resulting pattern matrices. The classification accuracies obtained in the derived feature space demonstrate that there does exist a good low-dimensional representation space. Methods to reduce the computational requirements, the primary limiting factor of this method, are discussed.
Hiding a face in a fingerprint image

With the wide spread utilization of biometric identification systems, establishing the authenticity of biometric data itself has emerged as an important research issue. We present a fingerprint image watermarking method that can embed facial information into host fingerprint images. This scheme has the advantage that in addition to fingerprint matching, the recovered face during the decoding can be used to establish the authenticity of the fingerprint and the user. By computing the ROC curves on a fingerprint database of 160 individuals, we show the advantages of the proposed watermarking scheme. Further, our scheme does not introduce any significant degradation in the fingerprint matching performance.
Benchmarking Fingerprint Minutiae Extractors

The performance of a fingerprint recognition system hinges on the errors introduced in each of its modules: image acquisition, preprocessing, feature extraction, and matching. One of the most critical and fundamental steps in fingerprint recognition is robust and accurate minutiae extraction. Hence we conduct a repeatable and controlled evaluation of one open-source and three commercial-off-the-shelf (COTS) minutiae extractors in terms of their performance in minutiae detection and localization. We also evaluate their robustness against controlled levels of image degradations introduced in the fingerprint images. Experiments were conducted on (i) a total of 3,458 fingerprint images from five public-domain databases, and (ii) 40,000 synthetically generated fingerprint images. The contributions of this study include: (i) a benchmark for minutiae extractors and minutiae interoperability, and (ii) robustness of minutiae extractors against image degradations.
Biometric recognition of newborns: Identification using palmprints

We present some results on newborn identification through high-resolution images of palmar surfaces. To our knowledge, there is no biometric system currently available that can be effectively used for newborn identification. The manual procedure of capturing inked footprints in practice for this purpose is limited for use inside hospitals and is not an effective solution for identification purposes. The use of friction ridge patterns on the hands of newborns is challenging due to both the small size of newborn's papillary ridges, which are, on average, 2.5 to 3 times smaller than the ridges in adult fingerprints, and their fragility, making them amenable to deformation. The proposed palmprint based automatic system for newborn identification is relatively easy to use and shows the feasibility of this approach. Experiments were performed on images collected from 250 newborns at the University Hospital (Universidade Federal do Paraná). An image acquisition protocol was developed in order to collect suitable images. When considering the good quality palmar images, the results show that the proposed approach is promising.
MRF model-based algorithms for image segmentation

The authors empirically compare three algorithms for segmenting simple, noisy images: simulated annealing (SA), iterated conditional modes (ICM), and maximizer of the posterior marginals (MPM). All use Markov random field (MRF) models to include prior contextual information. The comparison is based on artificial binary images which are degraded by Gaussian noise. Robustness is tested with correlated noise and with object and background textured. The ICM algorithm is evaluated when the degradation and model parameters must be estimated, in both supervised and unsupervised modes and on two real images. The results are assessed by visual inspection and through a numerical criterion. It is concluded that contextual information from MRF models improves segmentation when the number of categories and the degradation model are known and that parameters can be effectively estimated. None of the three algorithms is consistently best, but the ICM algorithm is the most robust. The energy of the a posteriori distribution is not always minimized at the best segmentation.<<ETX>>