nils-herrmann/clean_scirepeval huggingface dataset:
A k-nearest neighbor artificial neural network classifier

The authors propose an artificial neural network architecture to implement the k-nearest neighbor (k-NN) classifier. This architecture employs a k-maximum network which has some advantages over the 'winner-take-all' type of networks and other techniques used to select the maximum input. This k-maximum network has fewer interconnections than other networks, and is able to select exactly k maximum inputs as long as its (k-1)/sup th/ and k/sup th/ maximum inputs are distinct. The classification performance of the k-NN classifier is exactly the same as that of the traditional k-NN classifier. However, the parallelism of the network greatly reduces the computational requirement of the traditional k-NN classifier. Unlike the multilayer perceptrons which involve slowly converging back-propagation algorithms, the k-NN artificial neural network classifier does not need any training algorithm after the initial setting of the weights.<<ETX>>
Reject option for VQ-based Bayesian classification

We have developed a reject option for VQ-based supervised Bayesian classification to improve classification accuracy by sieving out patterns that are classified with a low confidence value. A small codebook extracted from a learning vector quantizer (LVQ) is used to estimate the class-conditional densities of the feature vector. We adapt the two commonly used rejection criteria, outlier rejection and ambiguity rejection, for the VQ-based Bayesian classifiers. Using three high-level image classification problems, we demonstrate how local rejection criteria can improve the error vs. reject characteristics of our classifier over a global rejection method.
Face Detection in Color Images

Human face detection plays an important role in applications such as video surveillance, human computer interface, face recognition, and face image database management. We propose a face detection algorithm for color images in the presence of varying lighting conditions as well as complex backgrounds. Based on a novel lighting compensation technique and a nonlinear color transformation, 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 face detection over a wide range of facial variations in color, position, scale, orientation, 3D pose, and expression in images from several photo collections (both indoors and outdoors).
A high performance SSL IPSEC protocol aware security processor

A 64M transistor security macro processor enables 40k full SSL handshakes per second with 1024 b RSA. The 3DES, AES, ARC4, SHA-1, MD5 and modular exponentiation cryptographic primitives are also supported. The processor is fabricated in a 0.13 /spl mu/m 8M CMOS process and consumes 12 W at 500 MHz.
A Case Study of Automated Face Recognition: The Boston Marathon Bombings Suspects

While face recognition can be a useful tool, helping authorities to narrow leads and confirm a criminal suspect's identity, the media tended to highlight the technology's limitations in the Boston Marathon bombings investigation. Consequently, we were motivated to reexamine the efficacy of unconstrained face recognition using this incident as a case study. We chose two automated face recognition systems known for their superior unconstrained matching performance based on tests conducted by the US National Institute of Standards and Technology: NEC's NeoFace v3.1 and Google-owned Pittsburgh Pattern Recognition v5.2.2. To conduct the experiment, we added six reference images of the Tsarnaevs taken from press releases and news articles following their identification to a background database of 1 million mug shots. Against this database, we searched for matches of the five face images of the brothers extracted by the FBI from surveillance, smartphone, or point-and-shoot camera footage before their identification.
Knowledge-based segmentation of Landsat images

A knowledge-based approach for Landsat image segmentation is proposed. The image segmentation problem is solved by extracting kernel information from the input image to provide an initial interpretation of the image and by using a knowledge-based hierarchical classifier to discriminate between major land-cover types in the study area. The proposed method is designed in such a way that a Landsat image can be segmented and interpreted without any prior image-dependent information. The general spectral land-cover knowledge is constructed from the training land-cover data, and the road information of an image is obtained through a road-detection program.
Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners

The effects of sample size on feature selection and error estimation for several types of classifiers are discussed. The focus is on the two-class problem. Classifier design in the context of small design sample size is explored. The estimation of error rates under small test sample size is given. Sample size effects in feature selection are discussed. Recommendations for the choice of learning and test sample sizes are given. In addition to surveying prior work in this area, an emphasis is placed on giving practical advice to designers and users of statistical pattern recognition systems.
FVC2000: Fingerprint Verification Competition

Reliable and accurate fingerprint recognition is a challenging pattern recognition problem, requiring algorithms robust in many contexts. FVC2000 competition attempted to establish the first common benchmark, allowing companies and academic institutions to unambiguously compare performance and track improvements in their fingerprint recognition algorithms. Three databases were created using different state-of-the-art sensors and a fourth database was artificially generated; 11 algorithms were extensively tested on the four data sets. We believe that FVC2000 protocol, databases, and results will be useful to all practitioners in the field not only as a benchmark for improving methods, but also for enabling an unbiased evaluation of algorithms.
Quad-Tree based Two Source Image Coding

In this paper we describe an adaptation of our two source RBC algorithm[1] where we introduce a variable block size. The blocks are chosen to meet some uniformity criterion and are generated via a quad-tree segmentation of the image which allows the segmentation overhead to be coded very efficiently. The prediction component is improved over standard RBC prediction with a fixed block size and some images may be coded using the prediction alone. However it is more economical to also code the residual component for more detailed images.
On the security of non-invertible fingerprint template transforms

Many transformation functions have been proposed for generating revocable or non-invertible biometric templates. However, their security analysis either ignores the distribution of biometric features or uses inefficient feature matching. This usually leads to unrealistic estimates of security. In this paper we introduce a new measure of non-invertibility, called the Coverage-Effort (CE) curve which measures the number of guesses (Effort) required by an adversary to recover a certain fraction (Coverage) of the original biometric data. In addition to utilizing the feature distribution, the CE curve allows estimation of security against partial recovery of biometric features. We analyze the CE curves obtained using different instances of a mixture of Gaussians based feature transform for fingerprint templates. Our analysis shows that knowledge of the fingerprint minutiae distribution reduces the effort required to obtain a specified coverage.
Artificial neural networks for feature extraction and multivariate data projection

Classical feature extraction and data projection methods have been well studied in the pattern recognition and exploratory data analysis literature. We propose a number of networks and learning algorithms which provide new or alternative tools for feature extraction and data projection. These networks include a network (SAMANN) for J.W. Sammon's (1969) nonlinear projection, a linear discriminant analysis (LDA) network, a nonlinear discriminant analysis (NDA) network, and a network for nonlinear projection (NP-SOM) based on Kohonen's self-organizing map. A common attribute of these networks is that they all employ adaptive learning algorithms which makes them suitable in some environments where the distribution of patterns in feature space changes with respect to time. The availability of these networks also facilitates hardware implementation of well-known classical feature extraction and projection approaches. Moreover, the SAMANN network offers the generalization ability of projecting new data, which is not present in the original Sammon's projection algorithm; the NDA method and NP-SOM network provide new powerful approaches for visualizing high dimensional data. We evaluate five representative neural networks for feature extraction and data projection based on a visual judgement of the two-dimensional projection maps and three quantitative criteria on eight data sets with various properties.
Visual surface reconstruction and boundary detection using stochastic models

The specification of regularization parameters is one of the difficult problems in using the weak membrane models for visual surface reconstruction and boundary detection. The gradient limit effect is a fundamental limitation of these models. The authors reduce the gradient limit effect by fusing the intensity and the range image of the same scene utilizing the Markov random field models. In order to improve the reconstruction the authors propose an extended weak membrane model that exhibits more complex interactions of the line process as well as the intensity and the depth processes. Consequently, the feasible regularization parameter space becomes larger, resulting in a considerably independent reconstruction on the model parameters. The performance of the introduced model is quantitatively evaluated by using a Kolmogorov-Smirnov difference measure.<<ETX>>
A Comparison of Human and Automated Face Verification Accuracy on Unconstrained Image Sets

Automatic face recognition technologies have seen significant improvements in performance due to a combination of advances in deep learning and availability of larger datasets for training deep networks. Since recognizing faces is a task that humans are believed to be very good at, it is only natural to compare the relative performance of automated face recognition and humans when processing fully unconstrained facial imagery. In this work, we expand on previous studies of the recognition accuracy of humans and automated systems by performing several novel analyses utilizing unconstrained face imagery. We examine the impact on performance when human recognizers are presented with varying amounts of imagery per subject, immutable attributes such as gender, and circumstantial attributes such as occlusion, illumination, and pose. Results indicate that humans greatly outperform state of the art automated face recognition algorithms on the challenging IJB-A dataset.
Fingerprint image synthesis based on statistical feature models

Fingerprint image synthesis has received considerable attention because of its potential use in generating large databases to evaluate the performance of fingerprint recognition systems. Existing fingerprint synthesis algorithms (e.g., SFinGe) focus on rendering realistic fingerprint images, but the features (e.g., minutiae) in these fingerprints are formed in an uncontrollable manner. However, generating synthetic fingerprint images with specified features is more useful in developing, evaluating and optimizing fingerprint recognition systems by providing ground truth features in the synthesized images. In this paper, we propose a method to synthesize fingerprint images that retain prespecified features (i.e., singular points, orientation field, and minutiae). To obtain realistic fingerprints, these features are sampled from appropriate statistical models which are trained by using real fingerprints in public domain databases. We validate the proposed method by comparing the synthesized images with those generated by SFinGe and by investigating the match score distributions on synthesized and real fingerprint databases. Furthermore, the synthesized fingerprint images and their minutiae are used to evaluate the matching capabilities of two commercial off-the-shelf (COTS) fingerprint matchers.
Goal-Directed Evaluation of Binarization Methods

This paper presents a methodology for evaluation of low-level image analysis methods, using binarization (two-level thresholding) as an example. Binarization of scanned gray scale images is the first step in most document image analysis systems. Selection of an appropriate binarization method for an input image domain is a difficult problem. Typically, a human expert evaluates the binarized images according to his/her visual criteria. However, to conduct an objective evaluation, one needs to investigate how well the subsequent image analysis steps will perform on the binarized image. We call this approach goal-directed evaluation, and it can be used to evaluate other low-level image processing methods as well. Our evaluation of binarization methods is in the context of digit recognition, so we define the performance of the character recognition module as the objective measure. Eleven different locally adaptive binarization methods were evaluated, and Niblack's method gave the best performance.
LFIQ: Latent fingerprint image quality

Latent fingerprint images are typically obtained under non-ideal acquisition conditions, resulting in incomplete or distorted impression of a finger, and ridge structure corrupted by background noise. This necessitates involving latent experts in latent fingerprint examination, including assessing the value of a latent print as forensic evidence. However, it is now generally agreed that human factors (e.g., human visual perception, expertise of latent examiners, workload, etc.) can significantly affect the reliability and consistency of the value determinations made by latent examiners. We propose an objective quality measure for latent fingerprints, called Latent Fingerprint Image Quality (LFIQ), that can be effectively used to distinguish latent fingerprints of good quality, which do not require any human intervention, and to compensate for the subjective nature of value determination by latent examiners. We investigate several factors that determine the latent quality: (i) ridge quality based on ridge clarity and connectivity of good ridge structures, (ii) minutiae reliability based on a minutiae dictionary learnt from high quality minutia patches, and (iii) position of the finger by detecting a reference point. The proposed LFIQ metric is based on triangulation of minutiae incorporating the above three factors. Experimental results show that (i) the proposed LFIQ is a good predictor of the latent matching performance by AFIS and (ii) it is also correlated with value determination by latent examiners.
FVC2002: Second Fingerprint Verification Competition

Two years after the first edition, a new Fingerprint Verification Competition (FVC2002) was organized by the authors, with the aim of determining the state-of-the-art in this challenging pattern recognition application. The experience and the feedback received from FVC2000 allowed the authors to improve the organization of FVC2002 and to capture the attention of a significantly higher number of academic and commercial organizations (33 algorithms were submitted). This paper discusses the FVC2002 database, the test protocol and the main differences between FVC2000 and FVC2002. The algorithm performance evaluation will be presented at the 16/sup th/ ICPR.
Dots and Incipients: Extended Features for Partial Fingerprint Matching

There are fundamental differences in the way fingerprints are compared by forensic examiners and current automatic systems. For example, while automatic systems focus mainly on the quantitative measures of fingerprint minutiae (ridge ending and bifurcation points), forensic examiners often analyze details of intrinsic ridge characteristics and relational information. This process, known as qualitative friction ridge analysis , includes examination of ridge shape, pores, dots, incipient ridges, etc. This explains the challenges that current automatic systems face in processing partial fingerprints, mostly seen in latents. The forensics and automatic fingerprint identification systems (AFIS) communities have been active in standardizing the definition of extended feature set, as well as quantifying the relevance and reliability of these features for automatic matching systems. CDEFFS (committee to define an extended feature set) has proposed a working draft on possible definitions and representations of extended features . However, benefits of utilizing these extended features in automatic systems are not yet known. While fingerprint matching technology is quite mature for matching tenprints , matching partial fingerprints, especially latents, still needs a lot of improvement. We propose an algorithm to extract two major level 3 feature types, dots and incipients, based on local phase symmetry and demonstrate their effectiveness in partial print matching. Since dots and incipients can be easily encoded by forensic examiners, we believe the results of this research will have benefits to next generation identification (NGI) systems.
A modified Hausdorff distance for object matching

The purpose of object matching is to decide the similarity between two objects. This paper introduces 24 possible distance measures based on the Hausdorff distance between two point sets. These measures can be used to match two sets of edge points extracted from any two objects. Based on experiments on synthetic images containing various levels of noise, the authors determined that one of these distance measures, called the modified Hausdorff distance (MHD) has the best performance for object matching. The advantages of MHD ever other distances are also demonstrated on several edge snaps of objects extracted from real images.
Texture segmentation on two high-performance computers

An implementation of a texture segmentation algorithm on two high-performance computers, the Connection Machine CM-2 and the Convex mini-supercomputer, is presented. Texture segmentation is the process of identifying regions with similar texture and separating regions with different textures and is one of the early steps towards identifying surfaces and objects in an image. A segmentation algorithm is described which first extracts texture tokens from the input image, then computes the Voronoi tessellation of the extracted tokens and measures shape features (moments of area) of the resulting Voronoi polygons. Feature similarity is used to obtain an initial labeling of texture tokens as interior or border with four quantized directions. This labeling is then refrained using probabilistic relaxation labeling. The computation of the Voronoi tessellation and the probabilistic relaxation labeling process, which are highly data-parallel procedures, are discussed. Substantial speedups were obtained over a sequential (Sun-4/280) implementation.<<ETX>>
Secure Face Unlock: Spoof Detection on Smartphones

With the wide deployment of the face recognition systems in applications from deduplication to mobile device unlocking, security against the face spoofing attacks requires increased attention; such attacks can be easily launched via printed photos, video replays, and 3D masks of a face. We address the problem of face spoof detection against the print (photo) and replay (photo or video) attacks based on the analysis of image distortion (e.g., surface reflection, moiré pattern, color distortion, and shape deformation) in spoof face images (or video frames). The application domain of interest is smartphone unlock, given that the growing number of smartphones have the face unlock and mobile payment capabilities. We build an unconstrained smartphone spoof attack database (MSU USSA) containing more than 1000 subjects. Both the print and replay attacks are captured using the front and rear cameras of a Nexus 5 smartphone. We analyze the image distortion of the print and replay attacks using different: 1) intensity channels (R, G, B, and grayscale); 2) image regions (entire image, detected face, and facial component between nose and chin); and 3) feature descriptors. We develop an efficient face spoof detection system on an Android smartphone. Experimental results on the public-domain Idiap Replay-Attack, CASIA FASD, and MSU-MFSD databases, and the MSU USSA database show that the proposed approach is effective in face spoof detection for both the cross-database and intra-database testing scenarios. User studies of our Android face spoof detection system involving 20 participants show that the proposed approach works very well in real application scenarios.
Efficient multi-label ranking for multi-class learning: Application to object recognition

Multi-label learning is useful in visual object recognition when several objects are present in an image. Conventional approaches implement multi-label learning as a set of binary classification problems, but they suffer from imbalanced data distributions when the number of classes is large. In this paper, we address multi-label learning with many classes via a ranking approach, termed multi-label ranking. Given a test image, the proposed scheme aims to order all the object classes such that the relevant classes are ranked higher than the irrelevant ones. We present an efficient algorithm for multi-label ranking based on the idea of block coordinate descent. The proposed algorithm is applied to visual object recognition. Empirical results on the PASCAL VOC 2006 and 2007 data sets show promising results in comparison to the state-of-the-art algorithms for multi-label learning.
Automatic segmentation of latent fingerprints

Latent fingerprints are routinely found at crime scenes due to the inadvertent contact of the criminals' finger tips with various objects. As such, they have been used as crucial evidence for identifying and convicting criminals by law enforcement agencies. However, compared to plain and rolled prints, latent fingerprints usually have poor quality of ridge impressions with small fingerprint area, and contain large overlap between the foreground area (friction ridge pattern) and structured or random noise in the background. Accordingly, latent fingerprint segmentation is a difficult problem. In this paper, we propose a latent fingerprint segmentation algorithm whose goal is to separate the fingerprint region (region of interest) from background. Our algorithm utilizes both ridge orientation and frequency features. The orientation tensor is used to obtain the symmetric patterns of fingerprint ridge orientation, and local Fourier analysis method is used to estimate the local ridge frequency of the latent fingerprint. Candidate fingerprint (foreground) regions are obtained for each feature type; an intersection of regions from orientation and frequency features localizes the true latent fingerprint regions. To verify the viability of the proposed segmentation algorithm, we evaluated the segmentation results in two aspects: a comparison with the ground truth foreground and matching performance based on segmented region.