nils-herrmann/clean_scirepeval huggingface dataset:
Learning Fingerprint Reconstruction: From Minutiae to Image

The set of minutia points is considered to be the most distinctive feature for fingerprint representation and is widely used in fingerprint matching. It was believed that the minutiae set does not contain sufficient information to reconstruct the original fingerprint image from which minutiae were extracted. However, recent studies have shown that it is indeed possible to reconstruct fingerprint images from their minutiae representations. Reconstruction techniques demonstrate the need for securing fingerprint templates, improving the template interoperability, and improving fingerprint synthesis. But, there is still a large gap between the matching performance obtained from original fingerprint images and their corresponding reconstructed fingerprint images. In this paper, the prior knowledge about fingerprint ridge structures is encoded in terms of orientation patch and continuous phase patch dictionaries to improve the fingerprint reconstruction. The orientation patch dictionary is used to reconstruct the orientation field from minutiae, while the continuous phase patch dictionary is used to reconstruct the ridge pattern. Experimental results on three public domain databases (FVC2002 DB1_A, FVC2002 DB2_A, and NIST SD4) demonstrate that the proposed reconstruction algorithm outperforms the state-of-the-art reconstruction algorithms in terms of both: 1) spurious minutiae and 2) matching performance with respect to type-I attack (matching the reconstructed fingerprint against the same impression from which minutiae set was extracted) and type-II attack (matching the reconstructed fingerprint against a different impression of the same finger).
Scars, marks and tattoos (SMT): Soft biometric for suspect and victim identification

Scars, marks and tattoos (SMT) are being increasingly used for suspect and victim identification in forensics and law enforcement agencies. Tattoos, in particular, are getting serious attention because of their visual and demographic characteristics as well as their increasing prevalence. However, current tattoo matching procedure requires human-assigned class labels in the ANSI/NIST ITL 1-2000 standard which makes it time consuming and subjective with limited retrieval performance. Further, tattoo images are complex and often contain multiple objects with large intra-class variability, making it very difficult to assign a single category in the ANSI/NIST standard. We describe a content-based image retrieval (CBIR) system for matching and retrieving tattoo images. Based on scale invariant feature transform (SIFT) features extracted from tattoo images and optional accompanying demographical information, our system computes feature-based similarity between the query tattoo image and tattoos in the criminal database. Experimental results on two different tattoo databases show encouraging results.
Learning kernel combination from noisy pairwise constraints

We consider the problem of learning the combination of multiple kernels given noisy pairwise constraints, which is in contrast to most of the existing studies that assume perfect pairwise constraints. This problem is particularly important when the pairwise constraints are derived from side information such as hyperlinks and paper citations. We propose a probabilistic approach for learning the combination of multiple kernels and show that under appropriate assumptions, the combination weights learned by the proposed approach from the noisy pairwise constraints converge to the optimal weights learned from perfectly labeled pairwise constraints. Empirical studies on data clustering using the learned combined kernel verify the effectiveness of the proposed approach.
Automated Latent Fingerprint Recognition

Latent fingerprints are one of the most important and widely used evidence in law enforcement and forensic agencies worldwide. Yet, NIST evaluations show that the performance of state-of-the-art latent recognition systems is far from satisfactory. An automated latent fingerprint recognition system with high accuracy is essential to compare latents found at crime scenes to a large collection of reference prints to generate a candidate list of possible mates. In this paper, we propose an automated latent fingerprint recognition algorithm that utilizes Convolutional Neural Networks (ConvNets) for ridge flow estimation and minutiae descriptor extraction, and extract complementary templates (two minutiae templates and one texture template) to represent the latent. The comparison scores between the latent and a reference print based on the three templates are fused to retrieve a short candidate list from the reference database. Experimental results show that the rank-1 identification accuracies (query latent is matched with its true mate in the reference database) are 64.7 percent for the NIST SD27 and 75.3 percent for the WVU latent databases, against a reference database of 100K rolled prints. These results are the best among published papers on latent recognition and competitive with the performance (66.7 and 70.8 percent rank-1 accuracies on NIST SD27 and WVU DB, respectively) of a leading COTS latent Automated Fingerprint Identification System (AFIS). By score-level (rank-level) fusion of our system with the commercial off-the-shelf (COTS) latent AFIS, the overall rank-1 identification performance can be improved from 64.7 and 75.3 to 73.3 percent (74.4 percent) and 76.6 percent (78.4 percent) on NIST SD27 and WVU latent databases, respectively.
LinkBoost: A Novel Cost-Sensitive Boosting Framework for Community-Level Network Link Prediction

Link prediction is a challenging task due to the inherent skew ness of network data. Typical link prediction methods can be categorized as either local or global. Local methods consider the link structure in the immediate neighborhood of a node pair to determine the presence or absence of a link, whereas global methods utilize information from the whole network. This paper presents a community (cluster) level link prediction method without the need to explicitly identify the communities in a network. Specifically, a variable-cost loss function is defined to address the data skew ness problem. We provide theoretical proof that shows the equivalence between maximizing the well-known modularity measure used in community detection and minimizing a special case of the proposed loss function. As a result, any link prediction method designed to optimize the loss function would result in more links being predicted within a community than between communities. We design a boosting algorithm to minimize the loss function and present an approach to scale-up the algorithm by decomposing the network into smaller partitions and aggregating the weak learners constructed from each partition. Experimental results show that our proposed Link Boost algorithm consistently performs as good as or better than many existing methods when evaluated on 4 real-world network datasets.
A background model initialization algorithm for video surveillance

Many motion detection and tracking algorithms rely on the process of background subtraction, a technique which detects changes from a model of the background scene. We present a new algorithm for the purpose of background model initialization. The algorithm takes as input a video sequence in which moving objects are present, and outputs a statistical background model describing the static parts of the scene. Multiple hypotheses of the background value at each pixel are generated by locating periods of stable intensity in the sequence. The likelihood of each hypothesis is then evaluated using optical flow information from the neighborhood around the pixel, and the most likely hypothesis is chosen to represent the background. Our results are compared with those of several standard background modeling techniques using surveillance video of humans in indoor environments.
Obtaining A Part-based 3-D Object Representation from Range Data

We examine two methods of obtaining a part-based 3-D object representation from range images. A small number (12) of shape types based on geolis are used as 3-D part primitives. The first method fits superquadric models to the depth data and then distinguishes between the shape classes by using statistical classifiers. Features for the classification are derived from the estimated superquadric parameters. Fairly good results are obtained using synthetic images. A second proposed method takes a differential-geometric approach. Surface segmentation of the range image is performed based on curvatures, and the part primitive is recognized based on the surface types and their adjacencies. Surfaces types and adjacencies of the part primitives also determine a small number of characteristic views to be stored in a multi-view representation of the primitives. This is to be used in cases where the surface types and adjacencies in the image yield more than one possibility for the part primitive.
A generative model for fingerprint minutiae

Fingerprint minutiae are the most important features used by latent fingerprint examiners, as well as in automated fingerprint recognition systems. Hence, understanding the statistical distribution of minutiae is essential in many fingerprint recognition related problems, such as fingerprint individuality and fingerprint synthesis. Prior work considers the occurrence of a minutia as a random event, and mostly assumes that individual minutiae are independent of each other. Some studies also considered the clustering tendency of minutiae and the minutiae neighborhood structures. Yet, it remains unclear whether the ridge orientation field has an impact on the minutiae occurrence. This paper investigates the correlation between ridge orientation field and minutiae. Assuming that minutiae are distributed conditionally on the variation in local ridge orientation, a new generative model is proposed for fingerprint minutiae. To evaluate the proposed model, we train the model using fingerprint images in the NIST SDl4 database, and simulate the minutiae in the fingerprints in the NIST SD4 database with the trained model. The experimental results show that by exploiting both the local ridge orientation variation and the neighborhood minutiae structure, the proposed model can better simulate the minutiae extracted from fingerprints than other models available in the literature.
A Sinusoidal Family of Unitary Transforms

A new family of unitary transforms is introduced. It is shown that the well-known discrete Fourier, cosine, sine, and the Karhunen-Loeve (KL) (for first-order stationary Markov processes) transforms are members of this family. All the member transforms of this family are sinusoidal sequences that are asymptotically equivalent. For finite-length data, these transforms provide different approximations to the KL transform of the said data. From the theory of these transforms some well-known facts about orthogonal transforms are easily explained and some widely misunderstood concepts are brought to light. For example, the near-optimal behavior of the even discrete cosine transform to the KL transform of first-order Markov processes is explained and, at the same time, it is shown that this transform is not always such a good (or near-optimal) approximation to the above-mentioned KL transform. It is also shown that each member of the sinusoidal family is the KL transform of a unique, first-order, non-stationary (in general), Markov process. Asymptotic equivalence and other interesting properties of these transforms can be studied by analyzing the underlying Markov processes.
Matching Composite Sketches to Face Photos: A Component-Based Approach

The problem of automatically matching composite sketches to facial photographs is addressed in this paper. Previous research on sketch recognition focused on matching sketches drawn by professional artists who either looked directly at the subjects (viewed sketches) or used a verbal description of the subject's appearance as provided by an eyewitness (forensic sketches). Unlike sketches hand drawn by artists, composite sketches are synthesized using one of the several facial composite software systems available to law enforcement agencies. We propose a component-based representation (CBR) approach to measure the similarity between a composite sketch and mugshot photograph. Specifically, we first automatically detect facial landmarks in composite sketches and face photos using an active shape model (ASM). Features are then extracted for each facial component using multiscale local binary patterns (MLBPs), and per component similarity is calculated. Finally, the similarity scores obtained from individual facial components are fused together, yielding a similarity score between a composite sketch and a face photo. Matching performance is further improved by filtering the large gallery of mugshot images using gender information. Experimental results on matching 123 composite sketches against two galleries with 10,123 and 1,316 mugshots show that the proposed method achieves promising performance (rank-100 accuracies of 77.2% and 89.4%, respectively) compared to a leading commercial face recognition system (rank-100 accuracies of 22.8% and 52.0%) and densely sampled MLBP on holistic faces (rank-100 accuracies of 27.6% and 10.6%). We believe our prototype system will be of great value to law enforcement agencies in apprehending suspects in a timely fashion.
Pores and Ridges: High-Resolution Fingerprint Matching Using Level 3 Features

Fingerprint friction ridge details are generally described in a hierarchical order at three different levels, namely, level 1 (pattern), level 2 (minutia points), and level 3 (pores and ridge contours). Although latent print examiners frequently take advantage of level 3 features to assist in identification, automated fingerprint identification systems (AFIS) currently rely only on level 1 and level 2 features. In fact, the Federal Bureau of Investigation's (FBI) standard of fingerprint resolution for AFIS is 500 pixels per inch (ppi), which is inadequate for capturing level 3 features, such as pores. With the advances in fingerprint sensing technology, many sensors are now equipped with dual resolution (500 ppi/1,000 ppi) scanning capability. However, increasing the scan resolution alone does not necessarily provide any performance improvement in fingerprint matching, unless an extended feature set is utilized. As a result, a systematic study to determine how much performance gain one can achieve by introducing level 3 features in AFIS is highly desired. We propose a hierarchical matching system that utilizes features at all the three levels extracted from 1,000 ppi fingerprint scans. Level 3 features, including pores and ridge contours, are automatically extracted using Gabor filters and wavelet transform and are locally matched using the iterative closest point (ICP) algorithm. Our experiments show that level 3 features carry significant discriminatory information. There is a relative reduction of 20 percent in the equal error rate (EER) of the matching system when level 3 features are employed in combination with level 1 and 2 features. This significant performance gain is consistently observed across various quality fingerprint images
Fingerprint warping using ridge curve correspondences

The performance of a fingerprint matching system is affected by the nonlinear deformation introduced in the fingerprint impression during image acquisition. This nonlinear deformation causes fingerprint features such as minutiae points and ridge curves to be distorted in a complex manner. A technique is presented to estimate the nonlinear distortion in fingerprint pairs based on ridge curve correspondences. The nonlinear distortion, represented using the thin-plate spline (TPS) function, aids in the estimation of an "average" deformation model for a specific finger when several impressions of that finger are available. The estimated average deformation is then utilized to distort the template fingerprint prior to matching it with an input fingerprint. The proposed deformation model based on ridge curves leads to a better alignment of two fingerprint images compared to a deformation model based on minutiae patterns. An index of deformation is proposed for selecting the "optimal" deformation model arising from multiple impressions associated with a finger. Results based on experimental data consisting of 1,600 fingerprints corresponding to 50 different fingers collected over a period of two weeks show that incorporating the proposed deformation model results in an improvement in the matching performance.
Large-scale parallel data clustering

Algorithmic enhancements are described that allow large reduction (for some data sets, over 95 percent) in the number of floating point operations in mean square error data clustering. These improvements are incorporated into a parallel data clustering tool, P-CLUSTER, developed in an earlier study. Experiments on segmenting standard texture images show that the proposed enhancements enable clustering of an entire 512/spl times/512 image at approximately the same computational cost as that of previous methods applied to only 5 percent of the image pixels.
Writer Adaptation for Online Handwriting Recognition

Writer-adaptation is the process of converting a writer-independent handwriting recognition system into a writer-dependent system. It can greatly increasing recognition accuracy, given adequate writer models. The limited amount of data a writer provides during training constrains the models' complexity. We show how appropriate use of writer-independent models is important for the adaptation. Our approach uses writer-independent writing style models (lexemes) to identify the styles present in a particular writer's training data. These models are then updated using the writer's data. Lexemes in the writer's data for which an inadequate number of training examples is available are replaced with the writer-independent models. We demonstrate the feasibility of this approach on both isolated handwritten character recognition and unconstrained word recognition tasks. Our results show an average reduction in error rate of 16.3 percent for lowercase characters as compared against representing each of the writer's character classes with a single model. In addition, an average error rate reduction of 9.2 percent is shown on handwritten words using only a small amount of data for adaptation.
Advances in Capturing Child Fingerprints: A High Resolution CMOS Image Sensor with SLDR Method

The use of biometrics technology for person identification has become prevalent due to several innovations as a result of research and development in this field. The purpose of this study is to find a solution which realizes newborn and infant (younger than 12 months old) fingerprint (hereinafter called as "child print") identification under operational difficulties, especially as it relates to the capturing process. The focus here will be on design and prototyping of a fingerprint sensor for overcoming the difficulties in capturing newborn and infant fingerprints that have previously been difficult to obtain.
Guidelines for best practices in biometrics research

Biometric recognition has undoubtedly made great strides over the past 50 years. To ensure that current academic research in biometrics has a positive impact on future technological developments, this paper documents some guidelines encouraging researchers to focus on high-impact problems, develop solutions that are practically viable, report results using sound experimental and evaluation protocols, and justify claims based on verifiable facts. The intent is to ensure that methods and results published in the literature have been properly evaluated and are practically feasible for automated or semi-automated human recognition. It is believed that following these guidelines will avoid inflated claims and support published research on a legitimate foundation that can be embraced by practitioners and peers in biometrics and related scientific disciplines (e.g, forensic science).
Validating a Biometric Authentication System: Sample Size Requirements

Authentication systems based on biometric features (e.g., fingerprint impressions, iris scans, human face images, etc.) are increasingly gaining widespread use and popularity. Often, vendors and owners of these commercial biometric systems claim impressive performance that is estimated based on some proprietary data. In such situations, there is a need to independently validate the claimed performance levels. System performance is typically evaluated by collecting biometric templates from n different subjects, and for convenience, acquiring multiple instances of the biometric for each of the n subjects. Very little work has been done in 1) constructing confidence regions based on the ROC curve for validating the claimed performance levels and 2) determining the required number of biometric samples needed to establish confidence regions of prespecified width for the ROC curve. To simplify the analysis that addresses these two problems, several previous studies have assumed that multiple acquisitions of the biometric entity are statistically independent. This assumption is too restrictive and is generally not valid. We have developed a validation technique based on multivariate copula models for correlated biometric acquisitions. Based on the same model, we also determine the minimum number of samples required to achieve confidence bands of desired width for the ROC curve. We illustrate the estimation of the confidence bands as well as the required number of biometric samples using a fingerprint matching system that is applied on samples collected from a small population
A form dropout system

This paper describes a system for form dropout when the filled-in characters or symbols are either touching or crossing the form frames and the form model is unknown. Since some of the character strokes are either touching or crossing the form frames, we need to address the following three issues: (i) localization of form frames; (ii) separation between characters and form frames, and (ii) reconstruction of broken strokes introduced during separation. The form frame is automatically located by finding long straight lines based on a data structure, called block adjacency graph. Form frame removal and character reconstruction are implemented in this graph. When the same process is applied to a blank form, followed by the procedure of connected component extraction and clustering, a form structure-based template is automatically generated which includes form model, skew angle and preprinted data areas. Given the form template, our system can extract both handwritten and machine-typed filled-in data. Experimental results on three different types of forms demonstrate the performance of our system.
Mobile robot localization via classification of multisensor maps

The authors solve the task of mobile robot localization through pattern classification of grid-based maps of important or interesting workspace regions. Each region is represented by registered ultrasound, vision, and infrared sensor grid maps; and feature-level sensor fusion is accomplished by extracting spatial descriptions from these maps. The coarse position of the robot is determined by classifying the map descriptions to recognize the workspace region that a given map represents. Using datasets collected from ten different rooms and ten different doorways in a building, the authors estimate a 94% recognition rate of the rooms and a 98% recognition rate of the doorways. The authors conclude that coarse position estimation in indoor domains is possible through classification of grid-based maps.<<ETX>>