nils-herrmann/clean_scirepeval huggingface dataset:
AutoMarkov DNNs for object classification

Recent advances in the area of Deep Convolutional Neural Networks have led to steady progress, mainly observed in the field of object classification and localization. Extensive testing helped generate frameworks guaranteeing the initiation of successful network architectures. For this reason, the authors focus on bringing added value on specific nodes of a generic network configuration. We propose a novel type of convolutional layer based on Autobinomial Markov-Gibbs Random Fields (AutoMarkov Layer). Our choice is motivated by the fact that each neuron in a layer is only connected to a local region in the following layer. This property allows us to integrate Markov Random Fields into the structure of a neuron, to account for the probability of each particular pathway. Functional testing is performed on the MNIST, CIFAR-10 and CIFAR-100 datasets, showing clear improvements for correct classification scores on all the datasets mentioned regardless of the network architecture.
Intensity-driven-adaptive-neighborhood technique for POLSAR parameters estimation

In this paper, a new method to estimate polarimetric coherency matrices and derive associated parameters is presented. For each pixel of the data set, an adaptive neighborhood is computed by a region growing technique driven exclusively by the intensity images. The three intensity images of the POLSAR acquisition are fused in the region growing process to ensure the stationarity hypothesis of the derived statistical population. Then, all pixels within the obtained adaptive neighborhood are, either complex averaged or estimated by the locally linear minimum mean squared error (LLMMSE), to yield a feature preserving reliable estimate of the polarimetric coherency matrix. The target entropy/alpha/anisotropy decomposition is applied on the derived polarimetric coherency matrix. Using this decomposition, unsupervised classifcation for land applications by an iterative algorithm based on a complex Wishart density function is employed. The method has been tested on airborne polarimetric synthetic aperture radar images (Northumberland Strait costal area - Canadian Space Agency).
A new inter-color distance based on color to planar shape mapping

Although simple and based on physical realizable color primaries, the RGB color space cannot allow the direct definition of a topology-preserving, perceptually-compatible inter-color distance. The classical normalized inter-color distances are rather complex to implement and tune. We propose the use of a planar color representation plot, derived from multivariate data visualization, which allows the introduction of a luminance-invariant, geometry-based inter-color distance, used with good results in the implementation of distance-based color filters.
Using Visual Rhythms for Detecting Video-Based Facial Spoof Attacks

Spoofing attacks or impersonation can be easily accomplished in a facial biometric system wherein users without access privileges attempt to authenticate themselves as valid users, in which an impostor needs only a photograph or a video with facial information of a legitimate user. Even with recent advances in biometrics, information forensics and security, vulnerability of facial biometric systems against spoofing attacks is still an open problem. Even though several methods have been proposed for photo-based spoofing attack detection, attacks performed with videos have been vastly overlooked, which hinders the use of the facial biometric systems in modern applications. In this paper, we present an algorithm for video-based spoofing attack detection through the analysis of global information which is invariant to content, since we discard video contents and analyze content-independent noise signatures present in the video related to the unique acquisition processes. Our approach takes advantage of noise signatures generated by the recaptured video to distinguish between fake and valid access videos. For that, we use the Fourier spectrum followed by the computation of video visual rhythms and the extraction of different characterization methods. For evaluation, we consider the novel unicamp video-attack database, which comprises 17 076 videos composed of real access and spoofing attack videos. In addition, we evaluate the proposed method using the replay-attack database, which contains photo-based and video-based face spoofing attacks.
First-person action recognition through Visual Rhythm texture description

First-person action recognition is a recent problem in computer vision, where an observer wears body cameras to understand and recognize actions from the captured video sequences. Technological advances have made it possible to offer small wearable cameras that can be attached onto bike helmets, belts, animal halters, among other accessories. Examples of potential applications include sports, security, healthcare, visual lifelogging, among others. In this paper, we propose a novel approach to first-person action recognition that consists in encoding video appearance, shape and motion information as visual rhythms and describing them through texture analysis. Experiments are conducted on the DogCentric Activity and JPL First-Person Interaction datasets, showing accuracy improvement over the baselines.
Face Spoofing Detection Through Visual Codebooks of Spectral Temporal Cubes

Despite important recent advances, the vulnerability of biometric systems to spoofing attacks is still an open problem. Spoof attacks occur when impostor users present synthetic biometric samples of a valid user to the biometric system seeking to deceive it. Considering the case of face biometrics, a spoofing attack consists in presenting a fake sample (e.g., photograph, digital video, or even a 3D mask) to the acquisition sensor with the facial information of a valid user. In this paper, we introduce a low cost and software-based method for detecting spoofing attempts in face recognition systems. Our hypothesis is that during acquisition, there will be inevitable artifacts left behind in the recaptured biometric samples allowing us to create a discriminative signature of the video generated by the biometric sensor. To characterize these artifacts, we extract time-spectral feature descriptors from the video, which can be understood as a low-level feature descriptor that gathers temporal and spectral information across the biometric sample and use the visual codebook concept to find mid-level feature descriptors computed from the low-level ones. Such descriptors are more robust for detecting several kinds of attacks than the low-level ones. The experimental results show the effectiveness of the proposed method for detecting different types of attacks in a variety of scenarios and data sets, including photos, videos, and 3D masks.
Face spoofing detection through partial least squares and low-level descriptors

Personal identity verification based on biometrics has received increasing attention since it allows reliable authentication through intrinsic characteristics, such as face, voice, iris, fingerprint, and gait. Particularly, face recognition techniques have been used in a number of applications, such as security surveillance, access control, crime solving, law enforcement, among others. To strengthen the results of verification, biometric systems must be robust against spoofing attempts with photographs or videos, which are two common ways of bypassing a face recognition system. In this paper, we describe an anti-spoofing solution based on a set of low-level feature descriptors capable of distinguishing between ‘live’ and ‘spoof’ images and videos. The proposed method explores both spatial and temporal information to learn distinctive characteristics between the two classes. Experiments conducted to validate our solution with datasets containing images and videos show results comparable to state-of-the-art approaches.
Scalar image interest point detection and description based on discrete Morse theory and geometric descriptors

The use of scalar data has arisen in many image applications which obtain data from simulated experiments, range scanners and photogrammetry. Their different nature of acquisition influences the type of processing and the analysis that may be undertaken in tasks such as registration, retrieval and recognition of structures or objects. This paper presents a new method for detecting and describing scale invariant descriptors over dense scalar data sets. The detection of interest points for each image is accomplished using an approach based on the Morse theory and descriptors are computed exploring the geometry of the data. Experiments are performed to demonstrate the effectiveness of the proposed method.
An Approach to Supporting Incremental Visual Data Classification

Automatic data classification is a computationally intensive task that presents variable precision and is considerably sensitive to the classifier configuration and to data representation, particularly for evolving data sets. Some of these issues can best be handled by methods that support users' control over the classification steps. In this paper, we propose a visual data classification methodology that supports users in tasks related to categorization such as training set selection; model creation, application and verification; and classifier tuning. The approach is then well suited for incremental classification, present in many applications with evolving data sets. Data set visualization is accomplished by means of point placement strategies, and we exemplify the method through multidimensional projections and Neighbor Joining trees. The same methodology can be employed by a user who wishes to create his or her own ground truth (or perspective) from a previously unlabeled data set. We validate the methodology through its application to categorization scenarios of image and text data sets, involving the creation, application, verification, and adjustment of classification models.
The 2nd competition on counter measures to 2D face spoofing attacks

As a crucial security problem, anti-spoofing in biometrics, and particularly for the face modality, has achieved great progress in the recent years. Still, new threats arrive inform of better, more realistic and more sophisticated spoofing attacks. The objective of the 2nd Competition on Counter Measures to 2D Face Spoofing Attacks is to challenge researchers to create counter measures effectively detecting a variety of attacks. The submitted propositions are evaluated on the Replay-Attack database and the achieved results are presented in this paper.
A verify-correct approach to person re-identification based on Partial Least Squares signatures

In the surveillance field, it is very common to have camera networks covering large crowded areas. Not rarely, cameras in these networks do not share the same field of view and they are not always calibrated. In these cases, common problems such as tracking cannot be directly applied as the information from one camera must be also consistent with the others. This is the most common scenario for the person re-identification problem, where there is the need to detect, track and keep a consistent identification of people across a network of cameras. Many approaches have been developed to solve this problem in different manners. However, person re-identification is still an open problem due to many challenges required to be addressed to build a robust system. To tackle the re-identification problem and improve the accuracy, we propose a novel approach based on Partial Least Squares signatures, which is based on the visual appearance of people. We demonstrate the method performance with experiments conducted on three public available data sets. Results show that our method overcome the chosen baseline on all data sets.
Fast Low Bit-Rate 3D Searchless Fractal Video Encoding

Video encoding techniques play an important role in data reduction. Fractal compression has received considerable attention in the past decades. While early methods presented prohibitively large encoding times, recent search less fractal encoders reduced this problem. A fast 3D purely fractal video encoder based on a flexible adaptive spatial subdivision data structure is proposed in this work. The method completely avoids any kind of search for a matching domain block and is capable of performing fast compression and decompression with high visual fidelity. Experimental results show that the developed approach outperforms the state-of-the-art x264 video encoder at very low bit rates in high motion video sequences in both structural dissimilarity measure and encoding time.
Recognition of occluded facial expressions based on CENTRIST features

Emotion recognition based on facial expressions plays an important role in numerous applications, such as affective computing, behavior prediction, human-computer interactions, psychological health services, interpersonal relations, and social monitoring. In this work, we describe and analyze an emotion recognition system based on facial expressions robust to occlusions through Census Transform Histogram (CENTRIST) features. Initially, occluded facial regions are reconstructed by applying Robust Principal Component Analysis (RPCA). CENTRIST features are extracted from the facial expression representation, as well as Local Binary Patterns (LBP), Local Gradient Coding (LGC) and an extended Local Gradient Coding (LGC-HD). Then, the feature vector is reduced through Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). For facial expression recognition, K-nearest neighbor (K-NN) and Support Vector Machine (SVM) classifiers are applied and tested. Experimental results on two public data sets demonstrated that the CENTRIST representation achieved competitive accuracy rates for occluded and non-occluded facial expressions compared to other state-of-the-art approaches available in the literature.
Classification of Pollen Grain Images Based on an Ensemble of Classifiers

The recognition of pollen grains is a challenging task since they are three-dimensional structures with complex morphological characteristics. Palynologists are responsible for studying pollen, spores and similar microscopic plant structures. In this work, we develop and analyze an automatic method for classification of pollen grain images based on a set of features and classifiers. Predictions of different classifiers are fused into an ensemble rule of majority voting. Experiments conducted on two datasets containing different types of pollen grains are used to demonstrate the effectiveness of the proposed approach.
Video summarization method based on the weber local descriptor

Video summarization plays an important role in providing a compact representation of large volumes of video sequences through the search for the most informative or representative portions of their content. In this work, we propose and analyze a novel video summarization method based on texture and color information to describe the video frames in order to produce an effective visual abstract of the video sequence. Experiments conducted on a data set containing distinct genres demonstrate that the proposed method is capable of generating competitive video summaries when compared to other approaches available in the literature.
Automatic extraction of topographic features using adaptive triangular meshes

A method is described for the extraction of morphological information from images approximated by triangular meshes. Topographic features such as peaks, pits, ridges, valleys, and planar regions are considered the basic descriptive surface elements and are defined in terms of the local organization of the triangles in the mesh. The approach is suitable for image analysis tasks, simplifying object recognition and scene interpretation. Several images have been used to demonstrate the performance of the proposed method.
Texture classification based on spatial dependence features using co-occurrence matrices and Markov random fields

This paper presents a method for classification of textures based on features obtained from co-occurrence matrices and Markov random fields. Two steps are performed to classify the images. Initially, the method recognizes the homogeneous regions (object interior) in the image. Regions consisting of dissimilar elements (transition between objects) are then properly identified and classified. Experimental results demonstrate the robustness of the method in terms of variation in region size and number of parameters.
Illuminant-Based Transformed Spaces for Image Forensics

In this paper, we explore transformed spaces, represented by image illuminant maps, to propose a methodology for selecting complementary forms of characterizing visual properties for an effective and automated detection of image forgeries. We combine statistical telltales provided by different image descriptors that explore color, shape, and texture features. We focus on detecting image forgeries containing people and present a method for locating the forgery, specifically, the face of a person in an image. Experiments performed on three different open-access data sets show the potential of the proposed method for pinpointing image forgeries containing people. In the two first data sets (DSO-1 and DSI-1), the proposed method achieved a classification accuracy of 94% and 84%, respectively, a remarkable improvement when compared with the state-of-the-art methods. Finally, when evaluating the third data set comprising questioned images downloaded from the Internet, we also present a detailed analysis of target images.
Modeling dense range images through fast polygonal approximations

A method for approximating range images by integrating triangular meshes and curvature information is presented. First, an adaptive filtering technique is applied to the original range image based on estimations of the surface curvature. This produces a collection of 3D points, which are triangulated in order to produce an initial mesh. The mesh is then refined through an efficient Delaunay triangulation algorithm. A new local error measure is used to select points to be inserted into the triangulation. Points tend to scatter in planar areas and to concentrate in high variation areas. The method allows representations to be retrieved at variables levels of accuracy, providing a natural way of multiresolution modeling. Some experimental results are presented to show that the proposed technique is effective to represent range images.
Textured Image Segmentation Based on Spatial Dependence using a Markov Random Field Model

Image segmentation is a primary step in many computer vision tasks. Although many segmentation methods have been proposed in the last decades, there is no generic method that can be applied in a great variety of images. This work presents a new image segmentation method using texture features extracted by wavelet transforms combined with spatial dependence modeled by a Markov random field (MRF). The method initially produces a coarse segmentation, which is refined through a relaxation method based on a new energy function. A set of textured images is used to demonstrate the effectiveness of the proposed method.
Deep Representations for Iris, Face, and Fingerprint Spoofing Detection

Biometrics systems have significantly improved person identification and authentication, playing an important role in personal, national, and global security. However, these systems might be deceived (or spoofed) and, despite the recent advances in spoofing detection, current solutions often rely on domain knowledge, specific biometric reading systems, and attack types. We assume a very limited knowledge about biometric spoofing at the sensor to derive outstanding spoofing detection systems for iris, face, and fingerprint modalities based on two deep learning approaches. The first approach consists of learning suitable convolutional network architectures for each domain, whereas the second approach focuses on learning the weights of the network via back propagation. We consider nine biometric spoofing benchmarks - each one containing real and fake samples of a given biometric modality and attack type - and learn deep representations for each benchmark by combining and contrasting the two learning approaches. This strategy not only provides better comprehension of how these approaches interplay, but also creates systems that exceed the best known results in eight out of the nine benchmarks. The results strongly indicate that spoofing detection systems based on convolutional networks can be robust to attacks already known and possibly adapted, with little effort, to image-based attacks that are yet to come.
Part-based representation and classification for face recognition

In recent years, we can observe an increasing use of biometric technology in our daily lives. Face recognition has several advantages over other biometric modalities, since that it is natural, nonintrusive, and it is a task that humans perform routinely and effortlessly. Following a recent trend in this research field, this paper focuses on a part-based face recognition, exploring and evaluating specific descriptions / classifications for each facial part. Experimental results obtained in three public datasets (AR Face, MUCT and XM2VTS), assessing 15 approaches in each facial part and two score fusion strategies, show that features and classifiers specific for facial part can improve the accuracy of biometric systems, achieving error rates close to zero in some cases, including scenarios where the false acceptance cases are critical.
Fast and scalable enrollment for face identification based on Partial Least Squares

Face recognition has received increased attention due to its application in biometrics and surveillance systems, emerging two main tasks, verification and identification of faces. The first one aims at accepting or rejecting an identity assigned to a correct face, whereas the second aims at, given an unknown probe face, finding the best identity to it from a gallery of known faces. For the face identification problem, discriminative approaches such as the one-against-all method have achieved higher accuracy than descriptive approaches such as eigenfaces. However, such methods have scalability issues when new subjects are enrolled in the gallery once it is necessary to rebuild all discriminative models to take into account the new individuals. This work describes and evaluates a novel method for making the process of gallery maintenance more efficient. This method employs an association between the one-against-some classification scheme, which differently from the one-against-all approach that considers a random subset of subjects as counterexamples, and the use of a priority queue to provide a scalable approach to enrolling new subjects to the gallery. Experimental results obtained by applying the proposed method on publicly available face data sets demonstrate its advantage when compared to the one-against-all approach.
Gender recognition from face images using a geometric descriptor

Gender recognition from face images is a challenging problem with applications in various knowledge domains, such as biometrics, security and surveillance, human-computer interaction, among others. In this work, we propose and evaluate a novel method for gender recognition based on a geometric descriptor constructed from a pre-defined face shape model. The proposed approach, tested on four different face datasets, achieved superior results for most cases when compared to other methods based on geometric descriptors.