nils-herrmann/clean_scirepeval huggingface dataset:
Gray scale processing of hydrographic maps

This paper investigates how gray scale information can be used in a hydrographic map understanding system to improve the system performance. To process gray scale scanned map images, we have implemented a topographic analysis method and a binary analysis method. In addition, deconvolution of the gray scale map image was used as an optional preprocessing step for both the methods. Both the methods process the input image by extracting binary print components, recognizing long lines, splitting touching digits and recognizing the digits. The topographic analysis extracts the information by computing topographic labels for each pixel, while the binary analysis is based on locally adaptive thresholding of the gray scale image. The performance of each method was evaluated by measuring the recognition performance of the digit recognition module. Experimental results indicate that the computationally intensive deconvolution and topographic analysis does not improve system performance. The same high performance is achieved by binary analysis, provided a high quality locally adaptive binary method is used.
Face Spoof Detection With Image Distortion Analysis

Automatic face recognition is now widely used in applications ranging from deduplication of identity to authentication of mobile payment. This popularity of face recognition has raised concerns about face spoof attacks (also known as biometric sensor presentation attacks), where a photo or video of an authorized person's face could be used to gain access to facilities or services. While a number of face spoof detection techniques have been proposed, their generalization ability has not been adequately addressed. We propose an efficient and rather robust face spoof detection algorithm based on image distortion analysis (IDA). Four different features (specular reflection, blurriness, chromatic moment, and color diversity) are extracted to form the IDA feature vector. An ensemble classifier, consisting of multiple SVM classifiers trained for different face spoof attacks (e.g., printed photo and replayed video), is used to distinguish between genuine (live) and spoof faces. The proposed approach is extended to multiframe face spoof detection in videos using a voting-based scheme. We also collect a face spoof database, MSU mobile face spoofing database (MSU MFSD), using two mobile devices (Google Nexus 5 and MacBook Air) with three types of spoof attacks (printed photo, replayed video with iPhone 5S, and replayed video with iPad Air). Experimental results on two public-domain face spoof databases (Idiap REPLAY-ATTACK and CASIA FASD), and the MSU MFSD database show that the proposed approach outperforms the state-of-the-art methods in spoof detection. Our results also highlight the difficulty in separating genuine and spoof faces, especially in cross-database and cross-device scenarios.
An efficient approach for clustering face images

Investigations that require the exploitation of large volumes of face imagery are increasingly common in current forensic scenarios (e.g., Boston Marathon bombing), but effective solutions for triaging such imagery (i.e., low importance, moderate importance, and of critical interest) are not available in the literature. General issues for investigators in these scenarios are a lack of systems that can scale to volumes of images of the order of a few million, and a lack of established methods for clustering the face images into the unknown number of persons of interest contained in the collection. As such, we explore best practices for clustering large sets of face images (up to 1 million here) into large numbers of clusters (approximately 200 thousand) as a method of reducing the volume of data to be investigated by forensic analysts. Our analysis involves a performance comparison of several clustering algorithms in terms of the accuracy of grouping face images by identity, run-time, and efficiency in representing large datasets of face images in terms of compact and isolated clusters. For two different face datasets, a mugshot database (PCSO) and the well known unconstrained dataset, LFW, we find the rank-order clustering method to be effective in clustering accuracy, and relatively efficient in terms of run-time.
Face recognition across time lapse: On learning feature subspaces

There is a growing interest in understanding the impact of aging on face recognition performance, as well as designing recognition algorithms that are mostly invariant to temporal changes. While some success has been made on this front, a fundamental questions has yet to be answered: do face recognition systems that compensate for the effects of aging compromise recognition performance for faces that have not undergone any aging? The studies in this paper help confirm that age invariant systems do seem to decrease performance in non-aging scenarios. This is demonstrated by performing training experiments on the largest face aging dataset studied in the literature to date (over 200,000 images from roughly 64,000 subjects). Further experiments conducted in this research help demonstrate the impact of aging on two leading commercial face recognition systems. We also determine the regions of the face that remain the most stable over time.
Learning-based object detection in cardiac MR images

An automated method for left ventricle detection in MR cardiac images is presented. Ventricle detection is the first step in a fully automated segmentation system used to compute volumetric information about the heart. Our method is based on learning the gray level appearance of the ventricle by maximizing the discrimination between positive and negative examples in a training set. The main differences from previously reported methods are feature definition and solution to the optimization problem involved in the learning process. Our method was trained on a set of 1,350 MR cardiac images from which 101,250 positive examples and 123,096 negative examples were generated. The detection results on a test set of 887 different images demonstrate an excellent performance: 98% detection rate, a false alarm rate of 0.05% of the number of windows analyzed (10 false alarms per image) and a detection time of 2 seconds per 256/spl times/256 image on a Sun Ultra 10 for an 8-scale search. The false alarms ore eventually eliminated by a position/scale consistency check along all the images that represent the same anatomical slice.
IARPA Janus Benchmark-B Face Dataset

Despite the importance of rigorous testing data for evaluating face recognition algorithms, all major publicly available faces-in-the-wild datasets are constrained by the use of a commodity face detector, which limits, among other conditions, pose, occlusion, expression, and illumination variations. In 2015, the NIST IJB-A dataset, which consists of 500 subjects, was released to mitigate these constraints. However, the relatively low number of impostor and genuine matches per split in the IJB-A protocol limits the evaluation of an algorithm at operationally relevant assessment points. This paper builds upon IJB-A and introduces the IARPA Janus Benchmark-B (NIST IJB-B) dataset, a superset of IJB-A. IJB-B consists of 1,845 subjects with human-labeled ground truth face bounding boxes, eye/nose locations, and covariate metadata such as occlusion, facial hair, and skintone for 21,798 still images and 55,026 frames from 7,011 videos. IJB-B was also designed to have a more uniform geographic distribution of subjects across the globe than that of IJB-A. Test protocols for IJB-B represent operational use cases including access point identification, forensic quality media searches, surveillance video searches, and clustering. Finally, all images and videos in IJB-B are published under a Creative Commons distribution license and, therefore, can be freely distributed among the research community.
Lane boundary detection using a multiresolution Hough transform

Lane boundary detection is the problem of estimating the geometric structure of the lane boundaries of a road based on the images grabbed by a camera on board a vehicle. We use the Hough transform to detect lane boundaries with a parabolic model under a variety of road pavement types, lane structures and weather conditions. In the three-dimensional Hough space, a parabolic curve is represented as a straight line. To simplify the computation, the parametric space can be divided into (i) a two-dimensional space measured by the parameters which are shared by all the lane edges, and (ii) a one-dimensional space of the parameter which makes a distinction among different edges in an image. A multiresolution strategy is used to improve both the speed and accuracy of the Hough transform. Experimental results show that the proposed method is relatively less prone to the image noise and is computationally tractable.
A Coarse to Fine Minutiae-Based Latent Palmprint Matching

With the availability of live-scan palmprint technology, high resolution palmprint recognition has started to receive significant attention in forensics and law enforcement. In forensic applications, latent palmprints provide critical evidence as it is estimated that about 30 percent of the latents recovered at crime scenes are those of palms. Most of the available high-resolution palmprint matching algorithms essentially follow the minutiae-based fingerprint matching strategy. Considering the large number of minutiae (about 1,000 minutiae in a full palmprint compared to about 100 minutiae in a rolled fingerprint) and large area of foreground region in full palmprints, novel strategies need to be developed for efficient and robust latent palmprint matching. In this paper, a coarse to fine matching strategy based on minutiae clustering and minutiae match propagation is designed specifically for palmprint matching. To deal with the large number of minutiae, a local feature-based minutiae clustering algorithm is designed to cluster minutiae into several groups such that minutiae belonging to the same group have similar local characteristics. The coarse matching is then performed within each cluster to establish initial minutiae correspondences between two palmprints. Starting with each initial correspondence, a minutiae match propagation algorithm searches for mated minutiae in the full palmprint. The proposed palmprint matching algorithm has been evaluated on a latent-to-full palmprint database consisting of 446 latents and 12,489 background full prints. The matching results show a rank-1 identification accuracy of 79.4 percent, which is significantly higher than the 60.8 percent identification accuracy of a state-of-the-art latent palmprint matching algorithm on the same latent database. The average computation time of our algorithm for a single latent-to-full match is about 141 ms for genuine match and 50 ms for impostor match, on a Windows XP desktop system with 2.2-GHz CPU and 1.00-GB RAM. The computation time of our algorithm is an order of magnitude faster than a previously published state-of-the-art-algorithm.
Is there any texture in the image?

Texture analysis methods have been used in various image processing tasks, such as image segmentation, recognition, shape analysis, texture synthesis, and image compression. When applying any of these methods, we assume that the input image has some textural characteristics. This paper addresses the problem of deciding whether an image has texture; in other words, whether texture-based methods are suitable for processing the image. We define a texture to have a spatially uniform distribution of local gray-value variations. A fast algorithm for detecting regions that have texture according to this definition is presented. The performance of the method is demonstrated on several synthetic and natural images.
Tooth contour extraction for matching dental radiographs

In dental biometrics, the contours of teeth in dental radiographs are utilized for human identification. However, in many images, the contours of teeth are fuzzy and only partially visible. To extract the contours from these images, we propose a method based on the active contour models. A new dynamic energy is proposed for the directional snake to discriminate boundaries of adjacent teeth. Matching result shows the contours extracted with this method perform better than traditional methods.
Multibiometric Cryptosystems Based on Feature-Level Fusion

Multibiometric systems are being increasingly de- ployed in many large-scale biometric applications (e.g., FBI-IAFIS, UIDAI system in India) because they have several advantages such as lower error rates and larger population coverage compared to unibiometric systems. However, multibiometric systems require storage of multiple biometric templates (e.g., fingerprint, iris, and face) for each user, which results in increased risk to user privacy and system security. One method to protect individual templates is to store only the secure sketch generated from the corresponding template using a biometric cryptosystem. This requires storage of multiple sketches. In this paper, we propose a feature-level fusion framework to simultaneously protect multiple templates of a user as a single secure sketch. Our main contributions include: (1) practical implementation of the proposed feature-level fusion framework using two well-known biometric cryptosystems, namery,fuzzy vault and fuzzy commitment, and (2) detailed analysis of the trade-off between matching accuracy and security in the proposed multibiometric cryptosystems based on two different databases (one real and one virtual multimodal database), each containing the three most popular biometric modalities, namely, fingerprint, iris, and face. Experimental results show that both the multibiometric cryptosystems proposed here have higher security and matching performance compared to their unibiometric counterparts.
Combining multiple weak clusterings

A data set can be clustered in many ways depending on the clustering algorithm employed, parameter settings used and other factors. Can multiple clusterings be combined so that the final partitioning of data provides better clustering? The answer depends on the quality of clusterings to be combined as well as the properties of the fusion method. First, we introduce a unified representation for multiple clusterings and formulate the corresponding categorical clustering problem. As a result, we show that the consensus function is related to the classical intra-class variance criterion using the generalized mutual information definition. Second, we show the efficacy of combining partitions generated by weak clustering algorithms that use data projections and random data splits. A simple explanatory model is offered for the behavior of combinations of such weak clustering components. We analyze the combination accuracy as a function of parameters controlling the power and resolution of component partitions as well as the learning dynamics vs. the number of clusterings involved. Finally, some empirical studies compare the effectiveness of several consensus functions.
Face recognition: Some challenges in forensics

Face recognition has become a valuable and routine forensic tool used by criminal investigators. Compared to automated face recognition, forensic face recognition is more demanding because it must be able to handle facial images captured under non-ideal conditions and it has high liability for following legal procedures. This paper discusses recent developments in automated face recognition that impact the forensic face recognition community. Improvements in forensic face recognition through research in facial aging, facial marks, forensic sketch recognition, face recognition in video, near-infrared face recognition, and use of soft biometrics will be discussed. Finally, current limitations and future research directions for face recognition in forensics are suggested.
3D model-assisted face recognition in video

Face recognition in video has gained wide attention as a covert method for surveillance to enhance security in a variety of application domains (e.g., airports). A video contains temporal information as well as multiple instances of a face, so it is expected to lead to better face recognition performance compared to still face images. However, faces appearing in a video have substantial variations in pose and lighting. These pose and lighting variations can be effectively modeled using 3D face models. Combining the advantages of 2D video and 3D face models, we propose a face recognition system that identifies faces in a video. The system utilizes the rich information in a video and overcomes the pose and lighting variations using 3D face model. The description of the proposed method and preliminary results are provided.
Face recognition with temporal invariance: A 3D aging model

The variation caused by aging has not received adequate attention compared with pose, lighting, and expression variations. Aging is a complex process that affects both the 3D shape of the face and its texture (e.g., wrinkles). While the facial age modeling has been widely studied in computer graphics community, only a few studies have been reported in computer vision literature on age-invariant face recognition. We propose an automatic aging simulation technique that can assist any existing face recognition engine for aging-invariant face recognition. We learn the aging patterns of shape and the corresponding texture in 3D domain by adapting a 3D morphable model to the 2D aging database (public domain FG-NET). At recognition time, each probe and all gallery images are modified to compensate for the age-induced variation using an intermediate 3D model deformation and a texture modification, prior to matching. The proposed approach is evaluated on a set of age-separated probe and gallery data using a state-of-the-art commercial face recognition engine, FaceVACS. Use of 3D aging model improves the rank-1 matching accuracy on FG-NET database from 28.0% to 37.8%, on average.
Automatic feature extraction for multiview 3D face recognition

Current 2D face recognition systems encounter difficulties in recognizing faces with large pose variations. Utilizing the pose-invariant features of 3D face data has the potential to handle multiview face matching. A feature extractor based on the directional maximum is proposed to estimate the nose tip location and the pose angle simultaneously. A nose profile model represented by subspaces is used to select the best candidates for the nose tip. Assisted by a statistical feature location model, a multimodal scheme is presented to extract eye and mouth corners. Using the automatic feature extractor, a fully automatic 3D face recognition system is developed. The system is evaluated on two databases, the MSU database (300 multiview test scans from 100 subjects) and the UND database (953 near frontal scans from 277 subjects). The automatic system provides recognition accuracy that is comparable to the accuracy of a system with manually labeled feature points
Incremental nonlinear dimensionality reduction by manifold learning

Understanding the structure of multidimensional patterns, especially in unsupervised cases, is of fundamental importance in data mining, pattern recognition, and machine learning. Several algorithms have been proposed to analyze the structure of high-dimensional data based on the notion of manifold learning. These algorithms have been used to extract the intrinsic characteristics of different types of high-dimensional data by performing nonlinear dimensionality reduction. Most of these algorithms operate in a "batch" mode and cannot be efficiently applied when data are collected sequentially. In this paper, we describe an incremental version of ISOMAP, one of the key manifold learning algorithms. Our experiments on synthetic data as well as real world images demonstrate that our modified algorithm can maintain an accurate low-dimensional representation of the data in an efficient manner.
Unsupervised Ensemble Ranking: Application to Large-Scale Image Retrieval

The continued explosion in the growth of image and video databases makes automatic image search and retrieval an extremely important problem. Among the various approaches to Content-based Image Retrieval (CBIR), image similarity based on local point descriptors has shown promising performance. However, this approach suffers from the scalability problem. Although bag-of-words model resolves the scalability problem, it suffers from loss in retrieval accuracy. We circumvent this performance loss by an ensemble ranking approach in which rankings from multiple bag-of-words models are combined to obtain more accurate retrieval results. An unsupervised algorithm is developed to learn the weights for fusing the rankings from multiple bag-of-words models. Experimental results on a database of 100,000 images show that this approach is both efficient and effective in finding visually similar images.
Clustering Face Carvings: Exploring the Devatas of Angkor Wat

We propose a framework for clustering and visualization of images of face carvings at archaeological sites. The pairwise similarities among face carvings are computed by performing Procrustes analysis on local facial features (eyes, nose, mouth, etc.). The distance between corresponding face features is computed using point distribution models; the final pairwise similarity is the weighted sum of feature similarities. A web-based interface is provided to allow domain experts to interactively assign different weights to each face feature, and display hierarchical clustering results in 2D or 3D projections obtained by multidimensional scaling. The proposed framework has been successfully applied to the devata goddesses depicted in the ancient Angkor Wat temple. The resulting clusterings and visualization will enable a systematic anthropological, ethnological and artistic analysis of nearly 1,800 stone portraits of devatas of Angkor Wat.
Object contour extraction using color and motion

A new approach to the extraction of the contour of a moving object is presented. The method is based on the integration of a motion segmentation technique using image substraction and a color segmentation technique based on the split-and-merge algorithm. The advantages of this method are: it can detect large moving objects and extract their boundaries; the background can be arbitrarily complicated and contain many non-moving objects occluded by the moving object; and it requires only three image frames that need not be consecutive, provided that the object is entirely contained in each of the three frames. The method is applied to a large number of color images of vehicles moving on a road and a highway ramp. The results are promising. The moving object boundaries are correctly extracted in 66 out of 73 test image sequences. The authors describe how this contour can be used as an input to a recognition system that classifies the vehicles into five generic categories. Of the 73 vehicles, 67 are correctly classified.<<ETX>>