nils-herrmann/clean_scirepeval huggingface dataset:
Fingerprint Spoof Buster: Use of Minutiae-Centered Patches

The primary purpose of a fingerprint recognition system is to ensure a reliable and accurate user authentication, but the security of the recognition system itself can be jeopardized by spoof attacks. This paper addresses the problem of developing accurate, generalizable, and efficient algorithms for detecting fingerprint spoof attacks. Specifically, we propose a deep convolutional neural network-based approach utilizing local patches centered and aligned using fingerprint minutiae. Experimental results on three public-domain LivDet datasets (2011, 2013, and 2015) show that the proposed approach provides the 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, in LivDet 2015, the proposed approach achieves 99.03% average accuracy over all sensors compared with 95.51% achieved by the LivDet 2015 competition winners. In addition, two new fingerprint presentation attack datasets containing more than 20,000 images, using two different fingerprint readers, and over 12 different spoof fabrication materials are collected. We also present a graphical user interface, called Fingerprint Spoof Buster, that allows the operator to visually examine the local regions of the fingerprint highlighted as live or spoof, instead of relying on only a single score as output by the traditional approaches.
An Efficient Two-Dimensional FFT Algorithm

A new version of the radix-2 row-column method for computing two-dimensional fast Fourier transforms is proposed. It uses a ``multiple vector'' FFT algorithm to compute the transforms of all the columns in an array simultaneously while avoiding all trivial multiplications. The minicomputer implementation of the algorithm runs faster than the 2 × 2 vector radix FFT algorithm. Analysis of the numbers of complex additions and multiplications required indicate that implementations of the radix-4 row-column FFT and 4 × 4 vector radix FFT on the same minicomputer would run slower than the multiple vector implementation.
Multisource classification of remotely sensed data: fusion of Landsat TM and SAR images

Proposes a new method for statistical classification of multisource data. The method is suited for land-use classification based on the fusion of remotely sensed images of the same scene captured at different dates from multiple sources. It incorporates a priori information about the likelihood of changes between the acquisition of the different images to be fused. A framework for the fusion of remotely sensed data based on a Bayesian formulation is presented. First, a simple fusion model is given, and then the basic model is extended to take into account the temporal attribute if the different data sources are acquired at different dates. The performance of the model is evaluated by fusing Landsat TM images and ERS-1-SAR images for land-use classification. The fusion model gives significant improvements in the classification error rates compared to the conventional single-source classifiers.
Face Tracking and Recognition at a Distance: A Coaxial and Concentric PTZ Camera System

Face recognition has been regarded as an effective method for subject identification at a distance because of its covert and remote sensing capability. However, face images have a low resolution when they are captured at a distance (say, larger than 5 meters) thereby degrading the face matching performance. To address this problem, we propose an imaging system consisting of static and pan-tilt-zoom (PTZ) cameras to acquire high resolution face images up to a distance of 12 meters. We propose a novel coaxial-concentric camera configuration between the static and PTZ cameras to achieve the distance invariance property using a simple calibration scheme. We also use a linear prediction model and camera motion control to mitigate delays in image processing and mechanical camera motion. Our imaging system was used to track 50 different subjects and their faces at distances ranging from 6 to 12 meters. The matching scenario consisted of these 50 subjects as probe and additional 10 000 subjects as gallery. Rank-1 identification accuracy of 91.5% was achieved compared to 0% rank-1 accuracy of the conventional camera system using a state-of-the-art matcher. The proposed camera system can operate at a larger distance (up to 50 meters) by replacing the static camera with a PTZ camera to detect a subject at a larger distance and control the second PTZ camera to capture the high-resolution face image.
A nonlinear projection method based on Kohonen's topology preserving maps

A nonlinear projection method is presented to visualize high-dimensional data as a 2D image. The proposed method is based on the topology preserving mapping algorithm of Kohonen. The topology preserving mapping algorithm is used to train a 2D network structure. Then the interpoint distances in the feature space between the units in the network are graphically displayed to show the underlying structure of the data. Furthermore, we present and discuss a new method to quantify how well a topology preserving mapping algorithm maps the high-dimensional input data onto the network structure. This is used to compare our projection method with a well-known method of Sammon (1969). Experiments indicate that the performance of the Kohonen projection method is comparable or better than Sammon's method for the purpose of classifying clustered data. Its time-complexity only depends on the resolution of the output image, and not on the size of the dataset. A disadvantage, however, is the large amount of CPU time required.
Object tracking using deformable templates

We propose a method for object tracking using prototype-based deformable template models. To track an object in an image sequence, we use a criterion which combines two terms: the frame-to-frame deviations of the object shape and the fidelity of the modeled shape to the input image. The deformable template model utilizes the prior shape information which is extracted from the previous frames along with a systematic shape deformation scheme to model the object shape in a new frame. The following image information is used in the tracking process: 1) edge and gradient information: the object boundary consists of pixels with large image gradient, 2) region consistency: the same object region possesses consistent color and texture throughout the sequence, and 3) interframe motion: the boundary of a moving object is characterized by large interframe motion. The tracking proceeds by optimizing an objective function which combines both the shape deformation and the fidelity of the modeled shape to the current image (in terms of gradient, texture, and interframe motion). The inherent structure in the deformable template, together with region, motion, and image gradient cues, makes the proposed algorithm relatively insensitive to the adverse effects of weak image features and moderate amounts of occlusion.
Registering Landsat images by point matching

Image registration of Landsat Thematic Mapper scenes with translational and rotational differences is studied. Two major steps of image registration, control-point selection and control-point matching, are emphasized. In control-point selection, the properties that a good control point should satisfy are defined. Several potential control-point candidates are suggested, and methods are discussed for extracting them from the input image. In control-point matching, a relaxation algorithm proposed in the literature is improved by reducing its time complexity from O(n/sup 4/) to O(n/sup 3/), where n is the number of control points. The matching algorithm also uses a two-way matching concept which utilizes the inherent symmetry property of the point-matching problem. The robustness of the proposed algorithm was demonstrated through simulation experiments by evaluating a matching index. Experimental results on Landsat images show that the proposed method produces results comparable to those obtained by an experienced photointerpreter.
Texture segmentation using Voronoi polygons

The authors have developed a texture-segmentation algorithm based on the Voronoi tessellation. The algorithm first builds the Voronoi tessellation of the tokens that make up the image. It then computes a set of features from the Voronoi polygons. Finally, it performs a probabilistic relaxation labeling on the tokens, to identify the interior and the border regions of the textures. The algorithm has successfully segmented images containing textures whose primitives have identical second order statistics.<<ETX>>
Automatic image orientation detection

We present an algorithm for automatic image orientation estimation using a Bayesian learning framework. We demonstrate that a small codebook (the optimal size of codebook is selected using a modified MDL criterion) extracted from a vector quantizer can be used to estimate the class-conditional densities of the observed features needed for the Bayesian methodology. We further show how feature clustering can be used as a feature selection mechanism to remove redundancies in the high-dimensional feature vectors used for classification. Experiments on a database of 17,901 images have shown that our proposed algorithm achieves an accuracy of approximately 97% on the training set and over 89% on an independent test set.
A Clustering Performance Measure Based on Fuzzy Set Decomposition

Clustering is primarily used to uncover the true underlying structure of a given data set and, for this purpose, it is desirable to subject the same data to several different clustering algorithms. This paper attempts to put an order on the various partitions of a data set obtained from different clustering algorithms. The goodness of each partition is expressed by means of a performance measure based on a fuzzy set decomposition of the data set under consideration. Several experiments reported in here show that the proposed performance measure puts an order on different partitions of the same data which is consistent with the error rate of a classifier designed on the basis of the obtained cluster labelings.
Bar code localization using texture analysis

Automatically locating a bar code on an object (e.g., a consumer product, postal mial, etc.) is an important step in an optical bar code recognition system. The authors present an approach for automatic bar code localization, which is based on the multichannel Gabor filtering technique. Both supervised and unsupervised methods are discussed. These methods are rotation-invariant and are able to locate bar codes on planar and curved surfaces.<<ETX>>
BONSAI: 3D object recognition using constrained search

A description is presented of BONSAI, a model-based 3-D object recognition system, which identifies and localizes 3-D objects in range images of one or more parts which have been designed on a CAD system. Recognition is performed via constrained search of the interpretation tree, using unary and binary constraints (derived automatically from the CAD models) to prune the search space. Experiments with over 200 images of 20 different parts demonstrate that the constrained search approach to 3-D object recognition has comparable accuracy to other existing systems.
2D matching of 3D moving objects in color outdoor scenes

This paper describes an object matching system which is able to extract objects of interest from outdoor scenes and match them. Our application (in the domain of IVHS) involves measuring the average travel time in a road network. The extraction of the object of interest is performed by fusing multiple cues including motion, color, edges, and model information. Two objects extracted from images captured by two independent cameras at different times are then matched to evaluate their similarity. Color indexing based on histogram matching is used to avoid matching all possible pairs of objects. To resolve ambiguities, further matching is done by measuring the Hausdorff distance between two sets of edge points. The object matching system was given 2 sets of 40 vehicles. It was able to identify 23 of the 30 correct matches and all the false matches were rejected. Color indexing reduced the number of candidates for a match from 40 to 2. This matching accuracy is adequate to obtain a reliable estimate of the average travel time.<<ETX>>
Matching 2.5D face scans to 3D models

The performance of face recognition systems that use two-dimensional images depends on factors such as lighting and subject's pose. We are developing a face recognition system that utilizes three-dimensional shape information to make the system more robust to arbitrary pose and lighting. For each subject, a 3D face model is constructed by integrating several 2.5D face scans which are captured from different views. 2.5D is a simplified 3D (x,y,z) surface representation that contains at most one depth value (z direction) for every point in the (x, y) plane. Two different modalities provided by the facial scan, namely, shape and texture, are utilized and integrated for face matching. The recognition engine consists of two components, surface matching and appearance-based matching. The surface matching component is based on a modified iterative closest point (ICP) algorithm. The candidate list from the gallery used for appearance matching is dynamically generated based on the output of the surface matching component, which reduces the complexity of the appearance-based matching stage. Three-dimensional models in the gallery are used to synthesize new appearance samples with pose and illumination variations and the synthesized face images are used in discriminant subspace analysis. The weighted sum rule is applied to combine the scores given by the two matching components. Experimental results are given for matching a database of 200 3D face models with 598 2.5D independent test scans acquired under different pose and some lighting and expression changes. These results show the feasibility of the proposed matching scheme.
On image classification: city vs. landscape

Grouping images into semantically meaningful categories using low-level visual features is a challenging and important problem in content-based image retrieval. Based on these groupings, effective indices can be built for an image database. The authors show how a specific high-level classification problem (city vs. landscape classification) can be solved from relatively simple low-level features suited for the particular classes. They have developed a procedure to qualitatively measure the saliency of a feature for classification problem based on the plot of the intra-class and inter-class distance distributions. They use this approach to determine the discriminative power of the following features: color histogram, color coherence vector DCT coefficient, edge direction histogram, and edge direction coherence vector. They determine that the edge direction-based features have the most discriminative power for the classification problem of interest. A weighted k-NN classifier is used for the classification. The classification system results in an accuracy of 93.9% when evaluated on an image database of 2,716 images using the leave-one-out method.
Face detection and facial feature extraction in color image

Face detection and facial feature extraction plays an important role in video surveillance, human computer interaction and face recognition. Color is a useful piece of information in computer vision especially for skin detection. In this paper, we propose a novel approach for skin segmentation and facial feature extraction. The proposed skin segmentation is a method for integrating the chrominance components of nonlinear YCrCb color model. The goal of skin detection is to group pixels to form possible face candidate regions and then use connected components analysis for pixels grouping. In order to detect the facial feature in scale invariant, the possible face candidate regions will be normalized, and then texture information in these regions will be segmented by means of mean and variance of face region. Edge will be detected using the method based on multi-scale morphological. Eye will be located by the PCA edge direction. The others feature, such as nose and mouth, also located using the geometrical shape information. As all the above-mentioned techniques are simple and efficient, the proposed method is computational effective and suitable for practical applications. In our experiments, the proposed method has been successfully evaluated using two different test datasets. The detection accuracy is around 98%, the average run time ranged from 0.1-0.3 sec per frame.
NFRAD: Near-Infrared Face Recognition at a Distance

Face recognition at a distance is gaining wide attention in order to augment the surveillance systems with face recognition capability. However, face recognition at a distance in nighttime has not yet received adequate attention considering the increased security threats at nighttime. We introduce a new face image database, called Near-Infrared Face Recognition at a Distance Database (NFRAD-DB). Images in NFRAD-DB are collected at a distance of up to 60 meters with 50 different subjects using a near-infrared camera, a telescope, and near-infrared illuminator. We provide face recognition performance using FaceVACS, DoG-SIFT, and DoG-MLBP representations. The face recognition test consisted of NIR images of these 50 subjects at 60 meters as probe and visible images at 1 meter with additional mug shot images of 10,000 subjects as gallery. Rank-1 identification accuracy of 28 percent was achieved from the proposed method compared to 18 percent rank-1 accuracy of a state of the art face recognition system, FaceVACS. These recognition results are encouraging given this challenging matching problem due to the illumination pattern and insufficient brightness in NFRAD images.
Uniformity testing using minimal spanning tree

Testing for uniformity of multivariate data is the initial step in exploratory pattern analysis. We propose a new uniformity testing method, which first computes the maximum (standardized) edge length in the MST of the given data. Large lengths indicate the existence of well-separated clusters or outliers in the data. For the data passing this edge inconsistency test, we generate two sub-samples of the data by a weighted re-sampling method, where the weights are computed based on the normalized edge lengths of MST of the entire data. The uniformity of the data is estimated by running the two-sample MST-test on these two sub-samples. Experiments with simulated and real data show the potential of the proposed test in identifying uniform or weakly clustered data. This test can also be used to rank various data sets based on their degree of uniformity.
Semicausal recursive filtering of images

A two dimensional discrete stochastic model for representing images is developed. This representation leads to a "hybrid" algorithm for spatial filtering of images. A "hybrid" algorithm is one where the filter equations are solved in a transform domain in one direction and in spatial domain in the other. Application of the model to linear filtering of images leads to scalar recursive filtering equations requiring only O(N2log2N) computations for N×N images. Examples on a 255 × 255 image are given.
Address block location on envelopes using Gabor filters: supervised method

The authors have implemented a texture-based supervised segmentation method to identify potential destination address blocks in envelope images. Texture features are computed by using a set of even symmetric Gabor filters. A one-layer neural network classifier is used to classify pixels into text and non-text categories using four texture features. The authors also present a simple heuristic to select the correct destination address block from among several candidates identified. The method works well on several envelope images.<<ETX>>
Latent orientation field estimation via convolutional neural network

The orientation field of a fingerprint is crucial for feature extraction and matching. However, estimation of orientation fields in latents is very challenging because latents are usually of poor quality. Inspired by the superiority of convolutional neural networks (ConvNets) for various classification and recognition tasks, we pose latent orientation field estimation in a latent patch to a classification problem, and propose a ConvNet based approach for latent orientation field estimation. The underlying idea is to identify the orientation field of a latent patch as one of a set of representative orientation patterns. To achieve this, 128 representative orientation patterns are learnt from a large number of orientation fields. For each orientation pattern, 10,000 fingerprint patches are selected to train the ConvNet. To simulate the quality of latents, texture noise is added to the training patches. Given image patches extracted from a latent, their orientation patterns are predicted by the trained ConvNet and quilted together to estimate the orientation field of the whole latent. Experimental results on NIST SD27 latent database demonstrate that the proposed algorithm outperforms the state-of-the-art orientation field estimation algorithms and can boost the identification performance of a state-of-the-art latent matcher by score fusion.
Recognition of unconstrained online Devanagari characters

Devanagari is a script used for several major languages such as Hindi, Sanskrit, Marathi and Nepali, and is used by more than 500 million people. Unconstrained Devanagari writing is more complex than English cursive due to the possible variations in the order, number, directional and shape of the constituent strokes. An online pen computing environment has numerous application in providing an easy human interface for a complex script like Devanagari. A Devanagari character recognition experiment with 20 different writers with each writer writing 5 samples of each character in a totally unconstrained way, has been conducted. An accuracy of 86.5% with no rejects is achieved through the combination of multiple classifiers that focus on either local online properties, or global off-line properties. Further improvements in performance are expected by using word-level contextual information. We also explore the use of writer dependent models to improve the recognition accuracy.
On reliable curvature estimation

An empirical study of the accuracy of five different curvature estimation techniques, using synthetic range images and images obtained from three range sensors, is presented. The results obtained highlight the problems inherent in accurate estimation of curvatures, which are second-order quantities, and thus highly sensitive to noise contamination. The numerical curvature estimation methods are found to perform about as accurately as the analytic techniques, although ensemble estimates of overall surface curvature such as averages are unreliable unless trimmed estimates are used. The median proved to be the best estimator of location. As an exception, it is shown theoretically that zero curvature can be fairly reliably detected, with appropriate selection of threshold values.