nils-herrmann/clean_scirepeval huggingface dataset:
Biometric Template Protection: Bridging the performance gap between theory and practice

Biometric recognition is an integral component of modern identity management and access control systems. Due to the strong and permanent link between individuals and their biometric traits, exposure of enrolled users' biometric information to adversaries can seriously compromise biometric system security and user privacy. Numerous techniques have been proposed for biometric template protection over the last 20 years. While these techniques are theoretically sound, they seldom guarantee the desired noninvertibility, revocability, and nonlinkability properties without significantly degrading the recognition performance. The objective of this work is to analyze the factors contributing to this performance divide and highlight promising research directions to bridge this gap. The design of invariant biometric representations remains a fundamental problem, despite recent attempts to address this issue through feature adaptation schemes. The difficulty in estimating the statistical distribution of biometric features not only hinders the development of better template protection algorithms but also diminishes the ability to quantify the noninvertibility and nonlinkability of existing algorithms. Finally, achieving nonlinkability without the use of external secrets (e.g., passwords) continues to be a challenging proposition. Further research on the above issues is required to cross the chasm between theory and practice in biometric template protection.
Stream Clustering: Efficient Kernel-Based Approximation Using Importance Sampling

Stream clustering methods, which group continuous, temporally ordered dynamic data instances, have been used in a number of applications such as stock market analysis, network analysis, and cosmological analysis. Most of the popular stream clustering algorithms are linear in nature, i.e. they assume that the data is linearly separable in the input space and use measures such as the Euclidean distance to define the inter-point similarity. Though these linear clustering algorithms are efficient, they do no achieve acceptable cluster quality on real-world data. Kernel-based clustering algorithms, which use non-linear similarity measures, yield better cluster quality, but are unsuitable for clustering data streams due to their high running time and memory complexity. We propose an efficient kernel-based clustering algorithm, called the Approximate Stream Kernel k-means, which uses importance sampling to sample a subset of the data stream, and clusters the entire stream based on each data point's similarity to the sampled data points in real-time. Every time a data point is sampled, the kernel matrix representing the similarity between the sampled points is updated, and projected to a low dimensional space spanned by its top eigenvectors. The data points are clustered in this low-dimensional space using the k-means algorithm. Thus, the Approximate Stream Kernel k-means algorithm performs clustering in linear time using kernel-based similarity. We show that only a small number of points need to be sampled from the data stream, and the resulting approximation error is well-bounded. Using several large benchmark data sets to simulate data streams, we demonstrate that the proposed algorithm achieves a significant speedup over other kernel-based clustering algorithms with minimal loss in cluster quality. We also demonstrate the practical applicability of the proposed algorithm by using it to efficiently find trending topics in the Twitter stream data.
Segmentation and Classification of Range Images

The recognition of objects in three-dimensional space is a desirable capability of a computer vision system. Range images, which directly measure 3-D surface coordinates of a scene, are well suited for this task. In this paper we report a procedure to detect connected planar, convex, and concave surfaces of 3-D objects. This is accomplished in three stages. The first stage segments the range image into ``surface patches'' by a square error criterion clustering algorithm using surface points and associated surface normals. The second stage classifies these patches as planar, convex, or concave based on a non-parametric statistical test for trend, curvature values, and eigenvalue analysis. In the final stage, boundaries between adjacent surface patches are classified as crease or noncrease edges, and this information is used to merge compatible patches to produce reasonable faces of the object(s). This procedure has been successfully applied to a large number of real and synthetic images, four of which we present in this paper.
Restoring Degraded Face Images: A Case Study in Matching Faxed, Printed, and Scanned Photos

We study the problem of restoring severely degraded face images such as images scanned from passport photos or images subjected to fax compression, downscaling, and printing. The purpose of this paper is to illustrate the complexity of face recognition in such realistic scenarios and to provide a viable solution to it. The contributions of this work are two-fold. First, a database of face images is assembled and used to illustrate the challenges associated with matching severely degraded face images. Second, a preprocessing scheme with low computational complexity is developed in order to eliminate the noise present in degraded images and restore their quality. An extensive experimental study is performed to establish that the proposed restoration scheme improves the quality of the ensuing face images while simultaneously improving the performance of face matching.
Is there a fingerprint pattern in the image?

A fingerprint orientation field has distinct characteristics which can differentiate fingerprints from any other flow patterns: it has a specific number of singular points (cores and deltas), the configuration of singular points follows a certain spatial distribution, and its global shape is like an arch. In this paper, we propose a global fingerprint orientation field model, represented in terms of ordinary differential equations, which does not require any prior information such as singular points or orientation of a fingerprint. Further, the model requires only a small number of polynomial terms to represent the global fingerprint orientation field. The coefficients of the model are found subject to the constraints on the total number of singular points (i.e., 0, 2, or 4) in a fingerprint. The proposed model is used to distinguish fingerprint images from non-fingerprint images and altered fingerprints by measuring the abnormality in the orientation field of the image.
Non-causal representations of finite discrete signals

A theory of non-causal interpolative representation of finite discrete signals is developed. It is shown that such representations give lower mean square error, entropy and rate distortion compared to standard Markov representations. Compared to initial value Markov models, the non-causal models lead to stable boundary value problems. Relationship with Karhunen Loeve (KL) expansion and Innovation representation is established. An alternate interpretation of Wiener filter and a fast algorithm for KL transform of first order Markov sequence are given. Applications to image coding and filtering are discussed. Examples are given to illustrate the new results obtained.
A 667 MHz RISC microprocessor containing a 6.0 ns 64 b integer multiplier

This microprocessor is optimized for the desktop. The chip contains architectural, circuit, and technology enhancements that include a 32 kB, 2-way set associative virtual instruction cache, a 16 kB, dual-read-ported, physical data cache, and advanced branch prediction. Circuit enhancements include a 6.0 ns integer multiplier, a 19.5 ns floating-point divider, and to support low jitter, 50% duty cycle 667 MHz clock, and an on-chip PLL. The 5.7M transistor microprocessor is fabricated in a 2.0 V, 0.28 /spl mu/m CMOS process with 4-layers of metal for interconnect, measures 1.0 cm/sup 2/, and supports a 2.5 V interface.
A hierarchical system for efficient image retrieval

Retrieval efficiency and accuracy are two important issues in designing a content-based database retrieval system. We propose a new image database retrieval method based on shape information. This system achieves both the desired efficiency and accuracy using a two-stage hierarchy: in the first stage, simple and easily computable statistical shape features are used to quickly browse through the database to generate a moderate number of plausible retrievals; in the second stage, the outputs from the first stage are screened using a deformable template matching process to discard spurious matches. We have tested the algorithm using hand drawn queries on a trademark database containing 1,100 images. Each retrieval takes a reasonable amount of computation time. The top most retrieved image from the system agrees with that obtained by human subjects, but there are significant differences between the top 10 retrieved images by our system and that provided by human subjects. This demonstrates the need for developing shape features that are better able to capture human perceptual similarity of shapes.
A Semicausal Model for Recursive Filtering of Two-Dimensional Images

A two-dimensional discrete stochastic model for representing images is developed. This representation has lower mean square error, compared to a standard autoregressive Markov representation. Application of the model to linear filtering of images degraded by white noise leads to scalar recursive filtering equations requiring only 0(N2log2N) computations for N x N images. The filter algorithm is a hybrid algorithm where the image is transformed along one dimension and spatially filtered, recursively, in the other. Examples on a 255 X 255 image are given.
Recognition of Digits in Hydrographic Maps: Binary Versus Topographic Analysis

Compares the performance of topographic analysis and binary analysis for recognition of digits in hydrographic maps. The performance of each method was measured by the correct classification rate of the final symbol recognition step when processing a complete hydrographic map of size 0.45/spl times/0.6 m/sup 2/ with about 35000 digits. The experimental results indicated that binary analysis had a better performance than topographic analysis. Overall, the performance of the binary analysis was acceptable.
A wrapper-based approach to image segmentation and classification

The traditional processing flow of segmentation followed by classification in computer vision assumes that the segmentation is able to successfully extract the object of interest from the background image. It is extremely difficult to obtain a reliable segmentation without any prior knowledge about the object that is being extracted from the scene. This is further complicated by the lack of any clearly defined metrics for evaluating the quality of segmentation or for comparing segmentation algorithms. We propose a method of segmentation that addresses both of these issues, by using the object classification subsystem as an integral part of the segmentation. This will provide contextual information regarding the objects to be segmented, as well as allow us to use the probability of correct classification as a metric to determine the quality of the segmentation. We view traditional segmentation as a filter operating on the image that is independent of the classifier, much like the filter methods for feature selection. We propose a new paradigm for segmentation and classification that follows the wrapper methods of feature selection. Our method wraps the segmentation and classification together, and uses the classification accuracy as the metric to determine the best segmentation. By using shape as the classification feature, we are able to develop a segmentation algorithm that relaxes the requirement that the object of interest to be segmented must be homogeneous in some low-level image parameter, such as texture, color, or grayscale. This represents an improvement over other segmentation methods that have used classification information only to modify the segmenter parameters, since these algorithms still require an underlying homogeneity in some parameter space. Rather than considering our method as, yet, another segmentation algorithm, we propose that our wrapper method can be considered as an image segmentation framework, within which existing image segmentation algorithms may be executed. We show the performance of our proposed wrapper-based segmenter on real-world and complex images of automotive vehicle occupants for the purpose of recognizing infants on the passenger seat and disabling the vehicle airbag. This is an interesting application for testing the robustness of our approach, due to the complexity of the images, and, consequently, we believe the algorithm will be suitable for many other real-world applications.
Writer adaptation of online handwriting models

Writer adaptation is the process of converting a writer-independent handwriting recognition system, which models the characteristics of a large group of writers, into a writer-dependent system, which is tuned for a particular writer. Adaptation has the potential of increasing recognition accuracies, provided adequate models can be constructed for a particular writer. The limited amount of data that a writer typically provides makes the role of writer-independent models crucial in the adaptation process. Our approach to writer-adaptation makes use of writer-independent writing style models (called lexemes), to identify the styles present in a particular writer's training data. These models are then retrained using the writer's data. We demonstrate the feasibility of this approach using hidden Markov models trained on a combination of discretely and cursively written lower case characters. Our results show an average reduction in error rate of 16.3% for lower case characters as compared against representing each of the writer's character classes with a single model.
Model-guided segmentation of corpus callosum in MR images

Magnetic resonance imaging (MRI) of the brain, followed by automated segmentation of the corpus callosum (CC) in midsagittal sections has important applications in neurology and neurocognitive research since the size and shape of the CC are shown to be correlated to sex, age, neurodegenerative diseases and various lateralized behavior in man. Moreover, whole head, multispectral 3D MRI recordings enable voxel-based tissue classification and estimation of total brain volumes, in addition to CC morphometric parameters. We propose a new algorithm that uses both multispectral MRI measurements (intensity values) and prior information about shape (CC template) to segment CC in midsagittal slices with very little user interaction. The algorithm has been successfully tested on a sample of 10 subjects scanned with multispectral 3D MRI, collected for a study of dyslexia. We conclude that the proposed method for CC segmentation is promising for clinical use when multispectral MR images are recorded.
Semantic face matching

The need for efficient methods for archiving and retrieving personal digital photo collections arises due to a significant increase in the number of digital images and videos that people have to manage. We propose a semantic face matching approach for managing consumer photographs based on semantic face attributes. These attributes are organized as a semantic face graph (derived from a 3D generic face model) containing facial components such as eyes and mouth in the spatial domain. We align the semantic facial components in the semantic face graph with the extracted facial features in a given image. Aligned facial components are transformed to a feature space spanned by Fourier descriptors of facial components for face matching. The semantic face graph allows face matching based on selected facial components. Our experimental results demonstrate that the proposed semantic representation of the face is useful for face matching and visualization (e.g., generating facial caricatures).
Deformation Analysis for 3D Face Matching

Current two-dimensional image based face recognition systems encounter difficulties with large facial appearance variations due to the pose, illumination and expression changes. Utilizing 3D information of human faces is promising to handle the pose and lighting variations. While the 3D shape of a face does not change due to head pose (rigid) and lighting changes, it is not invariant to the non-rigid facial movement and evolution, such as expressions and aging effect. We propose a face surface matching framework to take into account both rigid and non-rigid variations to match a 2.5D face image to a 3D face model. The rigid registration is achieved by a modified Iterative Closest Point (ICP) algorithm. The thin plate spline (TPS) model is applied to estimate the deformation displacement vector field, which is used to represent the non-rigid deformation. For the purpose of face matching, the non-rigid deformations from different sources are identified, which is formulated as a two-class classification problem: intra-subject deformation vs. inter-subject deformation. The deformation classification results are integrated with the matching distances to make the final decision. Experimental results on a database containing 100 3D face models and 98 2.5D scans with smiling expression show that the number of errors is reduced from 28 to 18.
On the monotonicity of the performance of Bayesian classifiers (Corresp.)

Even with a finite set of training samples, the performance of a Bayesian classifier can not be degraded by increasing the number of features, as long as the old features are recoverable from the new features. This is true even for the general Bayesian classifiers investigated by qq Hughes, a result which contradicts previous interpretations of Hughes' model. The reasons for these difficulties are discussed. It would appear that the peaking behavior of practical classifiers is caused principally by their nonoptimal use of the features.
Fingerprint spoof detection using minutiae-based local patches

The individuality of fingerprints is being leveraged for a plethora of day-to-day applications, ranging from unlocking a smartphone to international border security. While the primary purpose of a fingerprint recognition system is to ensure a reliable and accurate user authentication, the security of the recognition system itself can be jeopardized by spoof attacks. This study addresses the problem of developing accurate and generalizable algorithms for detecting fingerprint spoof attacks. We propose a deep convolutional neural network based approach utilizing local patches extracted around fingerprint minutiae. Experimental results on three public-domain LivDet datasets (2011, 2013, and 2015) show that the proposed approach provides state of the art accuracies in fingerprint spoof detection for intra-sensor, cross-material, cross-sensor, as well as cross-dataset testing scenarios. For example, the proposed approach achieves a 69% reduction in average classification error for spoof detection under both known material and cross-material scenarios on LivDet 2015 datasets.
A 32-core RISC microprocessor with network accelerators, power management and testability features

This paper describes our third generation multicore processor that exhibits a high level of integration. The current design doubles the number of cores, triples the frequency and more than quadruples total memory bandwidth over. It contains 700M transistors and has been fabricated in a 65nm process technology, with 10 layers of copper interconnect and C4 bumps. It contains 32 MIPS cores, 4MB of level 2 cache, multiple hardware accelerator units, 4 72b DDR3 memory controllers operating at 1600 MHz, 20 generic SerDes lanes up to 6.25Gb/s, additional network and boot interfaces and general purpose I/Os. Maximum frequency is 1.6GHz for cores and L2 cache. Excluding I/O, thermal design power ranges from 40W to 65W depending on the frequency bin.
Age estimation from face images: Human vs. machine performance

There has been a growing interest in automatic age estimation from facial images due to a variety of potential applications in law enforcement, security control, and human-computer interaction. However, despite advances in automatic age estimation, it remains a challenging problem. This is because the face aging process is determined not only by intrinsic factors, e.g. genetic factors, but also by extrinsic factors, e.g. lifestyle, expression, and environment. As a result, different people with the same age can have quite different appearances due to different rates of facial aging. We propose a hierarchical approach for automatic age estimation, and provide an analysis of how aging influences individual facial components. Experimental results on the FG-NET, MORPH Album2, and PCSO databases show that eyes and nose are more informative than the other facial components in automatic age estimation. We also study the ability of humans to estimate age using data collected via crowdsourcing, and show that the cumulative score (CS) within 5-year mean absolute error (MAE) of our method is better than the age estimates provided by humans.
Recognition of 3D free-form objects

We address the problem of recognizing 3D rigid free-form objects using dense range data when the objects can be imaged from arbitrary viewpoints and the objects vary in shape and complexity. We propose a multi-level matching strategy that employs shape spectral analysis and features derived from the COSMOS representations of free-form objects for fast and efficient recognition. We demonstrate that with a large model database of object views, a small set of ranked candidate matches can be selected quickly using shape spectrum based matching for further verification. We propose a graph-based matching scheme for view hypothesis verification using COSMOS representations of object views to establish the correct identity and the pose of the sensed object. Preliminary experimental results on a database containing views often different objects are shown to demonstrate the effectiveness of the COSMOS-based 3D object recognition system.
Object Matching Using Deformable Templates

We propose a general object localization and retrieval scheme based on object shape using deformable templates. Prior knowledge of an object shape is described by a prototype template which consists of the representative contour/edges, and a set of probabilistic deformation transformations on the template. A Bayesian scheme, which is based on this prior knowledge and the edge information in the input image, is employed to find a match between the deformed template and objects in the image. Computational efficiency is achieved via a coarse-to-fine implementation of the matching algorithm. Our method has been applied to retrieve objects with a variety of shapes from images with complex background. The proposed scheme is invariant to location, rotation, and moderate scale changes of the template.