nils-herrmann/clean_scirepeval huggingface dataset:
Soft Biometric Traits for Continuous User Authentication

Most existing computer and network systems authenticate a user only at the initial login session. This could be a critical security weakness, especially for high-security systems because it enables an impostor to access the system resources until the initial user logs out. This situation is encountered when the logged in user takes a short break without logging out or an impostor coerces the valid user to allow access to the system. To address this security flaw, we propose a continuous authentication scheme that continuously monitors and authenticates the logged in user. Previous methods for continuous authentication primarily used hard biometric traits, specifically fingerprint and face to continuously authenticate the initial logged in user. However, the use of these biometric traits is not only inconvenient to the user, but is also not always feasible due to the user's posture in front of the sensor. To mitigate this problem, we propose a new framework for continuous user authentication that primarily uses soft biometric traits (e.g., color of user's clothing and facial skin). The proposed framework automatically registers (enrolls) soft biometric traits every time the user logs in and fuses soft biometric matching with the conventional authentication schemes, namely password and face biometric. The proposed scheme has high tolerance to the user's posture in front of the computer system. Experimental results show the effectiveness of the proposed method for continuous user authentication.
A 1V 2mW 17GHz multi-modulus frequency divider based on TSPC logic using 65nm CMOS

We present a multi-modulus frequency divider based upon novel dual-modulus 4/5 and 2/3 true single-phase clocked (TSPC) prescalers. High-speed and low-power operation was achieved by merging the combinatorial counter logic with the flip-flop stages and removing circuit nodes at the expense of allowing a small short-circuit current during a short fraction of the operation cycle, thus minimizing the amount of nodes in the circuit. The divider is designed for operation in wireline or fibre-optic serial link transceivers with programmable divider ratios of 64, 80, 96, 100, 112, 120 and 140. The divider is implemented as part of a phase-locked loop around a quadrature voltage controlled oscillator in a 65nm CMOS technology. The maximum operating frequency is measured to be 17GHz with 2mW power consumption from a 1.0V supply voltage, and occupies 25×50μm2.
A VLSI Systolic Architecture for Pattern Clustering

Cluster analysis is a valuable tool in exploratory pattern analysis, especially when very little prior information about the data is available. In unsupervised pattern recognition and image segmentation applications, clustering techniques play an important role. The squared-error clustering technique is the most popular one among different clustering techniques. Due to the iterative nature of the squared-error clustering, it demands substantial CPU time, even for modest numbers of patterns. Recent advances in VLSI microelectronic technology triggered the idea of implementing the squared-error clustering directly in hardware. A two-level pipelined systolic pattern clustering array is proposed in this paper. The memory storage and access schemes are designed to enable a rhythmic data flow between processing units. Each processing unit is pipelined to further enhance the system performance. The total processing time for each pass of pattern labeling and cluster center updating is essentially dominated by the time required to fetch the pattern matrix once. Detailed architectural configuration, system performance evaluation, and simulation experiments are presented. The modularity and the regularity of the system architecture make it suited for VLSI implementations.
Integration of multiple feature groups and multiple views into a 3D object recognition system

Proposes two approaches for utilizing multiple-feature group (triples) and multiple-view information to reduce the number of hypotheses passed to the verification stage in an invariant feature indexing (IFI)-based object recognition system. The first approach is based on a majority voting scheme that keeps track of the number of consistent votes cast by prototype hypotheses for particular object models. The second approach examines the consistency of estimated object pose from multiple scene-triples of a single view or multiple views. Monte Carlo experiments employing 500 single-view synthetic range images and 195 pairs of synthetic range images with a large CAD-based 3D object database show that a significant number of hypotheses can be eliminated by using these approaches. The proposed approaches have also been tested on real range images of several objects. A salient feature of this system and experiment design compared to most existing 3D object recognition systems is the use of a large object data base and a large number of test images.<<ETX>>
An Operator Factorization Method for Restoration of Blurred Images

A problem of restoration of images blurred by space-invariant point-spread functions (SIPSF) is considered. The SIPSF operator is factorized as a sum of two matrices. The first term is a polynomial of a noncirculant operator P and the second term is a Hankel matrix which affects only the boundary observations. The image covariance matrix is also factorized into two terms; the covariance of the first term is a polynomial in P and the second term depends on the boundary values of the image. Thus, by modifying the image matrix by its boundary terms and the observations by the boundary observations, it is shown that the wieWir filter equation is a function of the operator P and can be solved exactly via the eigenvector expansion of P. The eigenvectors of the noncirculant matrix P are a set of orthronormal harmonic sinusoids called the sine transform, and the eigenvector expansion of the Wiener filter equation can be numerically achieved via a fast-sine-transform algorithm which is related to the fast-Fourier-transform (FFT) algorithm. The factorization therefore provides a fast Wiener restoration scheme for images and other random processes. Examples on 255 X 255 images are given.
Component-Based Representation in Automated Face Recognition

This paper presents a framework for component-based face alignment and representation that demonstrates improvements in matching performance over the more common holistic approach to face alignment and representation. This work is motivated by recent evidence from the cognitive science community demonstrating the efficacy of component-based facial representations. The component-based framework presented in this paper consists of the following major steps: 1) landmark extraction using Active Shape Models (ASM), 2) alignment and cropping of components using Procrustes Analysis, 3) representation of components with Multiscale Local Binary Patterns (MLBP), 4) per-component measurement of facial similarity, and 5) fusion of per-component similarities. We demonstrate on three public datasets and an operational dataset consisting of face images of 8000 subjects, that the proposed component-based representation provides higher recognition accuracies over holistic-based representations. Additionally, we show that the proposed component-based representations: 1) are more robust to changes in facial pose, and 2) improve recognition accuracy on occluded face images in forensic scenarios.
A Multichannel Approach to Fingerprint Classification

Fingerprint classification provides an important indexing mechanism in a fingerprint database. An accurate and consistent classification can greatly reduce fingerprint matching time for a large database. We present a fingerprint classification algorithm which is able to achieve an accuracy better than previously reported in the literature. We classify fingerprints into five categories: whorl, right loop, left loop, arch, and tented arch. The algorithm uses a novel representation (FingerCode) and is based on a two-stage classifier to make a classification. It has been tested on 4000 images in the NIST-4 database. For the five-class problem, a classification accuracy of 90 percent is achieved (with a 1.8 percent rejection during the feature extraction phase). For the four-class problem (arch and tented arch combined into one class), we are able to achieve a classification accuracy of 94.8 percent (with 1.8 percent rejection). By incorporating a reject option at the classifier, the classification accuracy can be increased to 96 percent for the five-class classification task, and to 97.8 percent for the four-class classification task after a total of 32.5 percent of the images are rejected.
Evaluation of Projection Algorithms

A number of linear and nonlinear mapping algorithms for the projection of patterns from a high-dimensional space to two dimensions are available. These two-dimensional representations allow quick visual observation of a data set. A combination of two popular mapping algorithms-Sammon's mean-square error technique and the triangulation method-is proposed to overcome the limitations in the individual algorithms. Some factors which describe the goodness of a projection are described, and a comparison is made of six of these algorithms by running them on four data sets. The results obtained support the use of the proposed algorithm.
A 300 MHz 64 b quad-issue CMOS RISC microprocessor

This quad-issue custom VLSI implementation of the Alpha architecture delivers 1200 MIPS/600 MFLOPS (peak) performance. The 16.5/spl times/18.1 mm/sup 2/ die contains 9.3 M transistors. It is built in a 3.3 V 4-layer metal, 0.5 /spl mu/m CMOS process. The upper metal layers are used primarily for power, clock, and global bus distribution. The chip is packaged in a 499-pin ceramic IPGA with 205 pins dedicated to VDD/VSS. The package includes a copper tungsten intrusive slug that provides a low thermal resistance between the die and a detachable heat sink. The chip is air cooled and dissipates 50 W at 300 MHz. 160 nF of on-chip decoupling capacitance controls power-supply noise.
Fingerprint Image Enhancement: Algorithm and Performance Evaluation

In order to ensure that the performance of an automatic fingerprint identification/verification system will be robust with respect to the quality of input fingerprint images, it is essential to incorporate a fingerprint enhancement algorithm in the minutiae extraction module. We present a fast fingerprint enhancement algorithm, which can adaptively improve the clarity of ridge and valley structures of input fingerprint images based on the estimated local ridge orientation and frequency. We have evaluated the performance of the image enhancement algorithm using the goodness index of the extracted minutiae and the accuracy of an online fingerprint verification system. Experimental results show that incorporating the enhancement algorithm improves both the goodness index and the verification accuracy.
Hierarchical kernel fitting for fingerprint classification and alignment

Fingerprint classification consists of labeling a fingerprint impression as one of several major types of fingerprints: arch, left loop, right loop, whorl, etc. The problem of fingerprint matching amounts to deciding whether or not two impressions were produced by the same finger. We propose a model based method for fingerprint classification which only uses the flow field, avoiding the non-trivial computation of the thinned ridges and minutia points. For each class, a fingerprint kernel is defined, which models the shape of fingerprints in that class. The classification is then achieved by finding the kernel that best fits the flow field of the given fingerprint. We obtain a classification accuracy of 91.25% on the NIST 4 database. We also show how the kernel fitting procedure can be used for fingerprint alignment.
COSMOS - A Representation Scheme for 3D Free-Form Objects

We address the problem of representing and recognizing 3D free-form objects when (1) the object viewpoint is arbitrary, (2) the objects may vary in shape and complexity, and (3) no restrictive assumptions are made about the types of surfaces on the object. We assume that a range image of a scene is available, containing a view of a rigid 3D object without occlusion. We propose a new and general surface representation scheme for recognizing objects with free-form (sculpted) surfaces. In this scheme, an object is described concisely in terms of maximal surface patches of constant shape index. The maximal patches that represent the object are mapped onto the unit sphere via their orientations, and aggregated via shape spectral functions. Properties such as surface area, curvedness, and connectivity, which are required to capture local and global information, are also built into the representation. The scheme yields a meaningful and rich description useful for object recognition. A novel concept, the shape spectrum of an object is also introduced within the framework of COSMOS for object view grouping and matching. We demonstrate the generality and the effectiveness of our scheme using real range images of complex objects.
A Multispectral Whole-Hand Biometric Authentication System

We describe the design and development of a prototype whole-hand imaging system. The sensor is based on multispectral technology that is able to provide hand shape, fingerprints and palmprint modalities of a user's hand by a single user interaction with the sensor. A clear advantage of our system over other unimodal sensors for these modalities include: (i) faster acquisition time, (ii) better quality images, and (iii) ability to provide spoof detection. Initial results on a medium-size database show good recognition performance based on individual modalities as well as after fusing multiple fingers and fusing finger and palm. The prototype is being refined in order to improve performance even further.
Latent Fingerprint Value Prediction: Crowd-Based Learning

Latent fingerprints are one of the most crucial sources of evidence in forensic investigations. As such, development of automatic latent fingerprint recognition systems to quickly and accurately identify the suspects is one of the most pressing problems facing fingerprint researchers. One of the first steps in manual latent processing is for a fingerprint examiner to perform a triage by assigning one of the following three values to a query latent: Value for Individualization (VID), Value for Exclusion Only (VEO), or No Value (NV). However, latent value determination by examiners is known to be subjective, resulting in large intra-examiner and inter-examiner variations. Furthermore, in spite of the guidelines available, the underlying bases that examiners implicitly use for value determination are unknown. In this paper, we propose a crowdsourcing based framework for understanding the underlying bases of value assignment by fingerprint examiners, and use it to learn a predictor for quantitative latent value assignment. Experimental results are reported using four latent fingerprint databases, two from forensic casework (NIST SD27 and MSP) and two collected in laboratory settings (WVU and IIITD), and a state-of-the-art latent automated fingerprint identification system (AFIS). The main conclusions of this paper are as follows: 1) crowdsourced latent value is more robust than prevailing value determination (VID, VEO, and NV) and latent fingerprint image quality for predicting AFIS performance; 2) two bases can explain expert value assignments, which can be interpreted in terms of latent features; and 3) our value predictor can rank a collection of latents from most informative to least informative.
Segmentation and Enhancement of Latent Fingerprints: A Coarse to Fine RidgeStructure Dictionary

Latent fingerprint matching has played a critical role in identifying suspects and criminals. However, compared to rolled and plain fingerprint matching, latent identification accuracy is significantly lower due to complex background noise, poor ridge quality and overlapping structured noise in latent images. Accordingly, manual markup of various features (e.g., region of interest, singular points and minutiae) is typically necessary to extract reliable features from latents. To reduce this markup cost and to improve the consistency in feature markup, fully automatic and highly accurate (“lights-out” capability) latent matching algorithms are needed. In this paper, a dictionary-based approach is proposed for automatic latent segmentation and enhancement towards the goal of achieving “lights-out” latent identification systems. Given a latent fingerprint image, a total variation (TV) decomposition model with L1 fidelity regularization is used to remove piecewise-smooth background noise. The texture component image obtained from the decomposition of latent image is divided into overlapping patches. Ridge structure dictionary, which is learnt from a set of high quality ridge patches, is then used to restore ridge structure in these latent patches. The ridge quality of a patch, which is used for latent segmentation, is defined as the structural similarity between the patch and its reconstruction. Orientation and frequency fields, which are used for latent enhancement, are then extracted from the reconstructed patch. To balance robustness and accuracy, a coarse to fine strategy is proposed. Experimental results on two latent fingerprint databases (i.e., NIST SD27 and WVU DB) show that the proposed algorithm outperforms the state-of-the-art segmentation and enhancement algorithms and boosts the performance of a state-of-the-art commercial latent matcher.
Automated Gland Segmentation and Classification for Gleason Grading of Prostate Tissue Images

The well-known Gleason grading method for an H&E prostatic carcinoma tissue image uses morphological features of histology patterns within a tissue slide to classify it into 5 grades. We have developed an automated gland segmentation and classification method that will be used for automated Gleason grading of a prostatic carcinoma tissue image. We demonstrate the performance of the proposed classification system for a three-class classification problem (benign, grade 3 carcinoma and grade 4 carcinoma) on a dataset containing 78 tissue images and achieve a classification accuracy of 88.84%. In comparison to the other segmentation-based methods, our approach combines the similarity of morphological patterns associated with a grade with the domain knowledge such as the appearance of nuclei and blue mucin for the grading task.
Recursive Block Coding - A New Approach to Transform Coding

The concept of fast KL transform coding introduced earlier [7],  for first-order Markov processes and certain random fields has been extended to higher order autoregressive (AR) sequences and practical images yielding what we call recursive block coding (RBC) algorithms. In general, the rate-distortion performance for these algorithms is significantly superior to that of the conventional block KL transform algorithm. Moreover, these algorithms permit the use of small size transforms, thereby removing the need for fast transforms and making the hardware implementation of such coders more appealing. This improved performance has been verified for practical image data and results in suppression of the block-boundary effect commonly observed in traditional transform coding techniques. This is illustrated by comparing RBC with cosine transform coding using both one- and twodimensional algorithms. Examples of RBC encoded images at various rates are given.
Sparse Decompositions for Exploratory Pattern Analysis

We define and verify the utility of a pattern analysis procedure called sparse decomposition. This technique involves sequentially ``peeling'' sparse subsets of patterns from a pattern set, where sparse subsets are sets of patterns which possess a certain degree of regularity or compactness as measured by a compactness measure c. If this is repeated until all patterns are deleted, then the sequence of decomposition ``layers'' derived by this procedure provides a wealth of information from which inferences about the original pattern set may be made. A statistic P is derived from this information and is shown to be powerful in detecting clustering tendency for data in reasonably compact sampling windows. The test is applied to both synthetic and real data.
Two-dimensional linear prediction models-part I: Spectral factorization and realization

In this paper we present several results for three different canonical forms of linear prediction on a plane. These filters have causal, semicausal, and noncausal prediction geometries. Starting from their properties we consider the problem of realization of these filters from a given power spectral density function (SDF). Since it is not possible in general to obtain rational spectral factors of a two-dimensional SDF, we propose algorithms for obtaining rational approximations which are stable and converge to their limit (irrational) factors as the order of approximation is increased. It is also shown that the normal equations associated with the minimum variance two-dimensional prediction filters give a useful algorithm for obtaining rational approximations which are stable and converge to their unique limit filters. This result allows design of finite-order stable filters by solving a finite number of equations while realizing the given SDF arbitrarily closely.
Fingerprint Recognition of Young Children

In 1899, Galton first captured ink-on-paper fingerprints of a single child from birth until the age of 4.5 years, manually compared the prints, and concluded that “the print of a child at the age of 2.5 years would serve to identify him ever after.” Since then, ink-on-paper fingerprinting and manual comparison methods have been superseded by digital capture and automatic fingerprint comparison techniques, but only a few feasibility studies on child fingerprint recognition have been conducted. Here, we present the first systematic and rigorous longitudinal study that addresses the following questions: 1) Do fingerprints of young children possess the salient features required to uniquely recognize a child? 2) If so, at what age can a child’s fingerprints be captured with sufficient fidelity for recognition? 3) Can a child’s fingerprints be used to reliably recognize the child as he ages? For this paper, we collected fingerprints of 309 children (0–5 years old) four different times over a one year period. We show, for the first time, that fingerprints acquired from a child as young as 6-h old exhibit distinguishing features necessary for recognition, and that state-of-the-art fingerprint technology achieves high recognition accuracy (98.9% true accept rate at 0.1% false accept rate) for children older than six months. In addition, we use mixed-effects statistical models to study the persistence of child fingerprint recognition accuracy and show that the recognition accuracy is not significantly affected over the one year time lapse in our data. Given rapidly growing requirements to recognize children for vaccination tracking, delivery of supplementary food, and national identification documents, this paper demonstrates that fingerprint recognition of young children (six months and older) is a viable solution based on available capture and recognition technology.