nils-herrmann/clean_scirepeval huggingface dataset:
Texture Segmentation Using Voronoi Polygons

Textures are defined in terms of primitives called tokens. A texture segmentation algorithm based on the Voronoi tessellation is discussed. The algorithm first builds the Voronoi tessellation of the tokens that make up the textured image. It then computes a feature vector for each Voronoi polygon. These feature vectors are used in a probabilistic relaxation labeling on the tokens, to identify the interior and the border regions of the textures. Some experimental results are shown.
Open source biometric recognition

The biometrics community enjoys an active research field that has produced algorithms for several modalities suitable for real-world applications. Despite these developments, there exist few open source implementations of complete algorithms that are maintained by the community or deployed outside a laboratory environment. In this paper we motivate the need for more community-driven open source software in the field of biometrics and present OpenBR as a candidate to address this deficiency. We overview the OpenBR software architecture and consider still-image frontal face recognition as a case study to illustrate its strengths and capabilities. All of our work is available at www.openbiometrics.org.
A feedback paradigm for latent fingerprint matching

Latent fingerprints are of critical value in forensic science because they serve as an important source of evidence in a court of law. Automatic matching of latent fingerprints to rolled/plain (exemplar) fingerprints with high accuracy is quite vital for such applications. However, due to poor latent image quality in general, latent fingerprint matching accuracy is far from satisfactory. In this research, we propose a novel latent matching paradigm which takes feedback from an exemplar print during matching to refine the features extracted from the latent. The refined latent features are then used to update the baseline match scores and resort the candidate list retrieved from the database. Experimental results show that the feedback based matching mechanism improves the rank-1 identification accuracy of the baseline latent matcher by about 8% and 3% for NIST SD27 and WVU latent databases, respectively. The proposed feedback paradigm can be wrapped around any latent matcher to improve its performance.
Automatic text location in images and video frames

Automatic text location (without character recognition capabilities) deals with extracting image regions that contain text only. The images of these regions can then be fed to an optical character recognition module or highlighted for users. This is very useful in a number of applications such as database indexing and converting paper documents to their electronic versions. The performance of our automatic text location algorithm is shown in several applications. Compared with some traditional text location methods, our method has the following advantages: 1) low computational cost; 2) robust to font size; and 3) high accuracy.
Deformation Modeling for Robust 3D Face Matching

Face recognition based on 3D surface matching is promising for overcoming some of the limitations of current 2D image-based face recognition systems. The 3D shape is generally invariant to the pose and lighting changes, but not invariant to the nonrigid facial movement such as expressions. Collecting and storing multiple templates to account for various expressions for each subject in a large database is not practical. We propose a facial surface modeling and matching scheme to match 2.5D facial scans in the presence of both nonrigid deformations and pose changes (multiview) to a stored 3D face model with neutral expression. A hierarchical geodesic-based resampling approach is applied to extract landmarks for modeling facial surface deformations. We are able to synthesize the deformation learned from a small group of subjects (control group) onto a 3D neutral model (not in the control group), resulting in a deformed template. A user-specific (3D) deformable model is built for each subject in the gallery with respect to the control group by combining the templates with synthesized deformations. By fitting this generative deformable model to a test scan, the proposed approach is able to handle expressions and pose changes simultaneously. A fully automatic and prototypic deformable model based 3D face matching system has been developed. Experimental results demonstrate that the proposed deformation modeling scheme increases the 3D face matching accuracy in comparison to matching with 3D neutral models by 7 and 10 percentage points, respectively, on a subset of the FRGC v2.0 3D benchmark and the MSU multiview 3D face database with expression variations.
A segmentation and object extraction algorithm with linear memory and time constraints

An experimental segmentation and object extraction algorithm is described. The system was developed for medical image processing with the primary application being DNA (deoxyribonucleic acid) sequencing. A typical DNA sequencing can involve processing the image of an autodiagram of size 14*17 inches resulting in a 2048*8600 digitized image under the specified spatial resolutions. The digitized image is too big to manage, even using super-minicomputers such as DEC VAX 11/780, and to perform any amount of classical image processing. Therefore, an elegant hardware and software design is necessary to deal with the large image and to complete the image-understanding task in an efficient manner. This work focuses on the image-processing aspects of the system and describes the run-length image representation, a link list data structure, a heuristic connected component analysis algorithm based on the data structure, a primitive object segmentation algorithm, and feature extraction.<<ETX>>
3D to 2D fingerprints: Unrolling and distortion correction

Touchless 3D fingerprint sensors can capture both 3D depth information and albedo images of the finger surface. Compared with 2D fingerprint images acquired by traditional contact-based fingerprint sensors, the 3D fingerprints are generally free from the distortion caused by non-uniform pressure and undesirable motion of the finger. Several unrolling algorithms have been proposed for virtual rolling of 3D fingerprints to obtain 2D equivalent fingerprints, so that they can be matched with the legacy 2D fingerprint databases. However, available unrolling algorithms do not consider the impact of distortion that is typically present in the legacy 2D fingerprint images. In this paper, we conduct a comparative study of representative unrolling algorithms and propose an effective approach to incorporate distortion into the unrolling process. The 3D fingerprint database was acquired by using a 3D fingerprint sensor being developed by the General Electric Global Research. By matching the 2D equivalent fingerprints with the corresponding 2D fingerprints collected with a commercial contact-based fingerprint sensor, we show that the compatibility between the 2D unrolled fingerprints and the traditional contact-based 2D fingerprints is improved after incorporating the distortion into the unrolling process.
Segmentation of document images

Several methods for segmentation of document images are explored. The authors pose the segmentation operation as a statistical classification task with two pattern classes: print and background. A number of classification strategies are available. All require some prior information about the distribution of gray levels for the two classes. Learning (either supervised or unsupervised) and automatic updating of the class-conditional densities are performed within image subregions to adapt global density estimates to the local area. After local densities have been obtained, each pixel within the window is classified; several techniques for this are considered. Results on four test images indicate that the commonly used contextual models are not suitable to all document images.
Live face video vs. spoof face video: Use of moiré patterns to detect replay video attacks

With the wide deployment of face recognition systems in applications from border control to mobile device unlocking, the combat of face spoofing attacks requires increased attention; such attacks can be easily launched via printed photos, video replays and 3D masks. We address the problem of facial spoofing detection against replay attacks based on the analysis of aliasing in spoof face videos. The application domain of interest is mobile phone unlock. We analyze the moiré pattern aliasing that commonly appears during the recapture of video or photo replays on a screen in different channels (R, G, B and grayscale) and regions (the whole frame, detected face, and facial component between the nose and chin). Multi-scale LBP and DSIFT features are used to represent the characteristics of moiré patterns that differentiate a replayed spoof face from a live face (face present). Experimental results on Idiap replay-attack and CASIA databases as well as a database collected in our laboratory (RAFS), which is based on the MSU-FSD database, shows that the proposed approach is very effective in face spoof detection for both cross-database, and intra-database testing scenarios.
A medical axis transform algorithm for compression and vectorization of document images

It is proposed that the medial axis transformation (MAT), being an invertible transformation, has value in lossless data compression, and, since the MAT conveys the line structure of an image, that it is also useful for vector conversion. To this end, some properties of the MAT are established and then exploited to devise a specialized chain-code representation. This MAT-based chain-code language captures any binary image but is particularly suited to line drawings. An extension of the language that can express chain-code sequences as linear and quadratic line segments under tunable error tolerance is described. The representation has achieved acceptable lossless data compression and significant additional compression for line drawings when vectorization is employed. As the error tolerance is increased, the amount of compression increases together with distortion. Thus, the method can be used to obtain a rate/distortion curve for binary images.<<ETX>>
Object tracking using deformable templates

We propose a novel 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 deviation of the object shape from its shape in the previous frame, and the fidelity of the detected shape to the input image. Shape and gradient information are used to track the object. We have also used the consistency between corresponding object regions throughout the sequence to help in trading the object of interest. Inter-frame motion is also used to track the boundary of moving objects. We have applied the algorithm to a number of image sequences from different sources. The inherent structure in the deformable template, together with region, motion, and image gradient cues, make the algorithm relatively insensitive to the adverse effects of weak image features and moderate partial occlusion.
Automatic Caption Localization in Compressed Video

We present a method to automatically localize captions in JPEG compressed images and the I-frames of MPEG compressed videos. Caption text regions are segmented from background images using their distinguishing texture characteristics. Unlike previously published methods which fully decompress the video sequence before extracting the text regions, this method locates candidate caption text regions directly in the DCT compressed domain using the intensity variation information encoded in the DCT domain. Therefore, only a very small amount of decoding is required. The proposed algorithm takes about 0.006 second to process a 240/spl times/350 image and achieves a recall rate of 99.17 percent while falsely accepting about 1.87 percent nontext DCT blocks on a variety of MPEG compressed videos containing more than 2,300 I-frames.
Parameter Estimation in Markov Random Field Contextual Models Using Geometric Models of Objects

We present a new scheme for the estimation of Markov random field line process parameters which uses geometric CAD models of the objects in the scene. The models are used to generate synthetic images of the objects from random view points. The edge maps computed from the synthesized images are used as training samples to estimate the line process parameters using a least squares method. We show that this parameter estimation method is useful for detecting edges in range as well as intensity edges. The main contributions of the paper are: 1) use of CAD models to obtain true edge labels which are otherwise not available; and 2) use of canonical Markov random field representation to reduce the number of parameters.
From Template to Image: Reconstructing Fingerprints from Minutiae Points

Most fingerprint-based biometric systems store the minutiae template of a user in the database. It has been traditionally assumed that the minutiae template of a user does not reveal any information about the original fingerprint. In this paper, we challenge this notion and show that three levels of information about the parent fingerprint can be elicited from the minutiae template alone, viz., 1) the orientation field information, 2) the class or type information, and 3) the friction ridge structure. The orientation estimation algorithm determines the direction of local ridges using the evidence of minutiae triplets. The estimated orientation field, along with the given minutiae distribution, is then used to predict the class of the fingerprint. Finally, the ridge structure of the parent fingerprint is generated using streamlines that are based on the estimated orientation field. Line integral convolution is used to impart texture to the ensuing ridges, resulting in a ridge map resembling the parent fingerprint. The salient feature of this noniterative method to generate ridges is its ability to preserve the minutiae at specified locations in the reconstructed ridge map. Experiments using a commercial fingerprint matcher suggest that the reconstructed ridge structure bears close resemblance to the parent
Three-dimensional model based face recognition

The performance of face recognition systems that use two-dimensional (2D) images is dependent on consistent conditions such as lighting, pose and facial expression. We are developing a multi-view face recognition system that utilizes three-dimensional (3D) information about the face to make the system more robust to these variations. This work describes a procedure for constructing a database of 3D face models and matching this database to 2.5D face scans which are captured from different views, using coordinate system invariant properties of the facial surface. 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. A robust similarity metric is defined for matching, based on an iterative closest point (ICP) registration process. Results are given for matching a database of 18 3D face models with 113 2.5D face scans.
Face detection in color images

Human face detection is often the first step in applications such as video surveillance, human computer interface, face recognition, and image database management. We propose a face detection algorithm for color images in the presence of varying lighting conditions as well as complex backgrounds. Our method detects skin regions over the entire image, and then generates face candidates based on the spatial arrangement of these skin patches. The algorithm constructs eye, mouth, and boundary maps for verifying each face candidate. Experimental results demonstrate successful detection over a wide variety of facial variations in color, position, scale, rotation, pose, and expression from several photo collections.
Dimensionality reduction using genetic algorithms

Pattern recognition generally requires that objects be described in terms of a set of measurable features. The selection and quality of the features representing each pattern affect the success of subsequent classification. Feature extraction is the process of deriving new features from original features to reduce the cost of feature measurement, increase classifier efficiency, and allow higher accuracy. Many feature extraction techniques involve linear transformations of the original pattern vectors to new vectors of lower dimensionality. While this is useful for data visualization and classification efficiency, it does not necessarily reduce the number of features to be measured since each new feature may be a linear combination of all of the features in the original pattern vector. Here, we present a new approach to feature extraction in which feature selection and extraction and classifier training are performed simultaneously using a genetic algorithm. The genetic algorithm optimizes a feature weight vector used to scale the individual features in the original pattern vectors. A masking vector is also employed for simultaneous selection of a feature subset. We employ this technique in combination with the k nearest neighbor classification rule, and compare the results with classical feature selection and extraction techniques, including sequential floating forward feature selection, and linear discriminant analysis. We also present results for the identification of favorable water-binding sites on protein surfaces.
Segmentation of Muscle Cell Pictures: A Preliminary Study

This paper describes a procedure for segmenting muscle cell pictures. The segmentation procedure is broken into two logical parts. The first part segments the picture into regions composed of cells or clumps of cells using a number of low-level operations. The second part of the procedure involves the segmentation of the cell clumps into individual cells. This is done by using a hierarchical clustering algorithm to group together those boundary points of a cell clump that belong to the same globally convex sections of the boundary. The dissimilarity measure used by the clustering algorithm is based only on information about the shape of the boundary, where this information is derived from line segments interior to the boundary. This procedure has given us satisfactory results on a number of test pictures.
Age-Invariant Face Recognition

One of the challenges in automatic face recognition is to achieve temporal invariance. In other words, the goal is to come up with a representation and matching scheme that is robust to changes due to facial aging. Facial aging is a complex process that affects both the 3D shape of the face and its texture (e.g., wrinkles). These shape and texture changes degrade the performance of automatic face recognition systems. However, facial aging has not received substantial attention compared to other facial variations due to pose, lighting, and expression. We propose a 3D aging modeling technique and show how it can be used to compensate for the age variations to improve the face recognition performance. The aging modeling technique adapts view-invariant 3D face models to the given 2D face aging database. The proposed approach is evaluated on three different databases (i.g., FG-NET, MORPH, and BROWNS) using FaceVACS, a state-of-the-art commercial face recognition engine.
Fingerprint mosaicking

It has been observed that the reduced contact area offered by solid-state fingerprint sensors does not provide sufficient information (e.g., number of minutiae) for high accuracy user verification. Further, multiple impressions of the same finger acquired by these sensors, may have only a small region of overlap thereby degrading the matching performance of the verification system. To deal with this problem, we have developed a fingerprint mosaicking scheme that constructs a composite fingerprint template using multiple impressions. A composite template reduces storage, improves matching time and alleviates the problem of template selection. In the proposed algorithm, two impressions (templates) of a finger are initially aligned using the corresponding minutiae points. This alignment is used by a modified version of the well-known iterative closest point algorithm (ICP) to compute a transformation matrix that defines the spatial relationship between the two impressions. The resulting transformation matrix is used in two ways: (a) the two templates are stitched together to generate a composite image. Minutiae points are then detected in this composite image; (b) the minutia maps obtained from each of the individual impressions are integrated to create a larger minutia map. Our experiments show that a composite template improves the performance of the fingerprint matching system by ∼ 4%.
CAD-based inspection of 3D objects using range images

Presents a CAD-based inspection system for defect detection in range images of castings. The inspection algorithms are applicable to a class of objects, specifically to objects that contain planar or quadric surfaces, rather than being specialized to a single object. CAD model information is used in several stages of the system, including surface classification and inspection. Techniques for detection of common casting defects and for inspection of manufacturing features and dimensional tolerances are presented. The methods are applied to a large number of real range images of castings with real defects. The inspection system is able to correctly classify over 90% of the casting images.<<ETX>>
A longitudinal study of automatic face recognition

With the deployment of automatic face recognition systems for many large-scale applications, it is crucial that we gain a thorough understanding of how facial aging affects the recognition performance, particularly across a large population. Because aging is a complex process involving genetic and environmental factors, some faces “age well” while the appearance of others changes drastically over time. This heterogeneity (inter-subject variability) suggests the need for a subject-specific aging analysis. In this paper, we conduct such an analysis using a longitudinal database of 147,784 operational mug shots of 18,007 repeat criminal offenders, where each subject has at least five face images acquired over a minimum of five years. By fitting multilevel statistical models to genuine similarity scores from two commercial-off-the-shelf (COTS) matchers, we quantify (i) the population average rate of change in genuine scores with respect to the elapsed time between two face images, and (ii) how closely the subject-specific rates of change follow the population average. Longitudinal analysis of the scores from the more accurate COTS matcher shows that despite decreasing genuine scores over time, the average subject can still be correctly verified at a false accept rate (FAR) of 0.01% across all 16 years of elapsed time in our database. We also investigate (i) the effects of several other covariates (gender, race, face quality), and (ii) the probability of true acceptance over time.