nils-herrmann/clean_scirepeval huggingface dataset:
A pilot study on affective classification of facial images for emerging news topics

The proliferation of news reports published in online websites and news information sharing among social media users necessitates effective techniques for analysing the image, text and video data related to news topics. This paper presents the first study to classify affective facial images on emerging news topics. The proposed system dynamically monitors and selects the current hot (of great interest) news topics with strong affective interestingness using textual keywords in news articles and social media discussions. Images from the selected hot topics are extracted and classified into three categorized emotions, positive, neutral and negative, based on facial expressions of subjects in the images. Performance evaluations on two facial image datasets collected from realworld resources demonstrate the applicability and effectiveness of the proposed system in affective classification of facial images in news reports. Facial expression shows high consistency with the affective textual content in news reports for positive emotion, while only low correlation has been observed for neutral and negative. The system can be directly used for applications, such as assisting editors in choosing photos with a proper affective semantic for a certain topic during news report preparation.
Application of ground penetrating radar technology for near-surface interface determination in coal mining

The use of ground penetrating radar (GPR) for detecting near-surface interfaces is a scenario of special interest to the underground coal mining industry. The problem is difficult to solve in practice because the radar echo is often dominated by unwanted components such as antenna crosstalk and ringing, ground-bounce effects, clutter, and severe attenuation. These nuisance components are also highly sensitive to subtle variations in ground conditions, rendering the application of standard signal pre-processing techniques largely ineffective in the unsupervised case. As a solution to this problem, we develop a novel algorithm which utilizes a pattern recognition-based approach using features derived from the bispectrum of the radar data. We show that, unlike traditional second order correlation based methods such as matched filtering which fail in known conditions, the new method reliably allows the determination of layer interfaces using GPR to be extended to the near surface region.
Effect of Pupil Dilation and Constriction on the Distribution of Bit Errors within the Iris

Texture information in the iris image is not uniform in discriminatory information content for biometric identity verification. The bits in an iris code obtained from the image differ in their consistency from one sample to another for the same identity. In this work, errors in bit strings are systematically analysed in order to investigate the effect of light-induced and drug-induced pupil dilation and constriction on the consistency of iris texture information. The statistics of bit errors are computed for client and impostor distributions as functions of radius and angle. Under normal conditions, a V-shaped radial trend of decreasing bit errors towards the central region of the iris is obtained for client matching, and it is observed that the distribution of errors as a function of angle is uniform. When iris images are affected by pupil dilation or constriction the radial distribution of bit errors is altered. A decreasing trend from the pupil outwards is observed for constriction, whereas a more uniform trend is observed for dilation. The main increase in bit errors occurs closer to the pupil in both cases.
Texture classification using gabor energy features and higher order spectral features: a comparative study

Several approaches to the classification and segmentation of textural content in digital images have been investigated in recent years. The extraction of features for classification has particularly received considerable attention. In this paper we contrast between two approaches for feature extraction i.e. Gabor filters and bispectral invariant features. A subset of the Brodatz album are used in a dichotomy experiment and separate SVMs are trained to classify features from each pair. Our experiments show that the bispectral invariant features produce better classification results for more texture pairs than the Gabor filters. Results also indicate that a combination of the two feature sets will yield higher accuracy, and for some texture pairs, neither works very well.
A Multi-Class Tracker Using a Scalable Condensation Filter

Tracking systems are typically targeted towards tracking a single class of object. In many real world situations, and in the ETISEO evaluation, it is advantageous to be able to track multiple classes of objects. In this paper we describe the adaptation of a single class tracking system to a multi-class tracking system, and describe a modified version of the condensation filter that can be used to track all objects, of all classes. We show that by using simple targeted detectors, we can achieve accurate tracking and can accurately distinguish between classes.
Biometric Based Cryptographic Key Generation from Faces

Existing asymmetric encryption algorithms require the storage of the secret private key. Stored keys are often protected by poorly selected user passwords that can either be guessed or obtained through brute force attacks. This is a weak link in the overall encryption system and can potentially compromise the integrity of sensitive data. Combining biometrics with cryptography is seen as a possible solution but any biometric cryptosystem must be able to overcome small variations present between different acquisitions of the same biometric in order to produce consistent keys. This paper discusses a new method which uses an entropy based feature extraction process coupled with Reed-Solomon error correcting codes that can generate deterministic bit-sequences from the output of an iterative one-way transform. The technique is evaluated using 3D face data and is shown to reliably produce keys of suitable length for 128-bit Advanced Encryption Standard (AES).
Content-Based Image (object) Retrieval with Rotational Invariant Bag-of-Visual Words representation

While Bag of Visual Word (BoW) approaches have achieved better retrieval performance and known as a precise representation, the performance is limited as it ignores spatial information. This paper presents a simple yet effective Rotation Invariant Bag of Visual Words (RIBoW) approach which encodes spatial information to achieve effective Content-Based Image Retrieval (CBIR) especially in object based retrieval. RIBoW approach uses circular image decomposition in combination with a simple shifting operation to achieve invariance in rotation using global image descriptors. Feature vectors are histograms of visual words in the traditional BoW approach. The given feature indices and cluster indices are together used to generate the signatures of the proposed method. The retrieval quality of the proposed approach is empirically evaluated using two benchmark datasets for image classification (a subset of the Corel dataset) and compared. Proposed approach is further validated with Caltech 256 dataset. The performance of the proposed approach confirms its effectiveness and robustness for rotation invariant image retrieval by transcending retrieval performance when comparing with BoW approaches.
Object Recognition Using Stereo Vision and Higher Order Spectra

This paper presents a novel technique to conduct correspondence-free stereo processing. Traditional stereo processing estimates structure through computationally expensive correspondence algorithms resulting in the generation of depth maps. If the purpose of estimating structure is to recognise objects, the depth maps (structural information) need to be further processed to produce features which can then be used for recognition. This paper outlines an alternative technique that bypasses the correspondence algorithms to combine the estimation of depth and feature extraction into one step. This is achieved through the application of Higher Order Spectra (HOS) analysis. Results are presented that demonstrate a Top 2 Rank identification rate of 80% for a small database of human faces.
Asphalt Concrete Surfaces Macrotexture Determination From Still Images

Road surface macrotexture is identified as one of the factors contributing to the surface's skid resistance. Existing methods of quantifying the surface macrotexture, such as the sand patch test and the laser profilometer test, are either expensive or intrusive, requiring traffic control. High-resolution cameras have made it possible to acquire good quality images from roads for the automated analysis of texture depth. In this paper, a granulometric method based on image processing is proposed to estimate road surface texture coarseness distribution from their edge profiles. More than 1300 images were acquired from two different sites, extending to a total of 2.96 km. The images were acquired using camera orientations of 60° and 90°. The road surface is modeled as a texture of particles, and the size distribution of these particles is obtained from chord lengths across edge boundaries. The mean size from each distribution is compared with the sensor measured texture depth obtained using a laser profilometer. By tuning the edge detector parameters, a coefficient of determination of up to R2 = 0.94 between the proposed method and the laser profilometer method was obtained. The high correlation is also confirmed by robust calibration parameters that enable the method to be used for unseen data after the method has been calibrated over road surface data with similar surface characteristics and under similar imaging conditions.
The development of a new signal processing program at the Queensland University of Technology

The School of Electrical and Electronic Systems Engineering at Queensland University of Technology, Brisbane, Australia (QUT), offers three bachelor degree courses in electrical and computer engineering. In all its courses there is a strong emphasis on signal processing. A newly established Signal Processing Research Centre (SPRC) has played an important role in the development of the signal processing units in these courses. This paper describes the unique design of the undergraduate program in signal processing at QUT, the laboratories developed to support it, and the criteria that influenced the design.
Toward a more robust facial expression recognition in occluded images using randomly sampled Gabor based templates

Occlusion is a big challenge for facial expression recognition (FER) in real-world situations. Previous FER efforts to address occlusion suffer from loss of appearance features and are largely limited to a few occlusion types and single testing strategy. This paper presents a robust approach for FER in occluded images and addresses these issues. A set of Gabor based templates is extracted from images in the gallery using a Monte Carlo algorithm. These templates are converted into distance features using template matching. The resulting feature vectors are robust to occlusion. Occluded eyes and mouth regions and randomly places occlusion patches are used for testing. Two testing strategies analyze the effects of these occlusions on the overall recognition performance as well as each facial expression. Experimental results on the Cohn-Kanade database confirm the high robustness of our approach and provide useful insights about the effects of occlusion on FER. Performance is also compared with previous approaches.
Integration strategies for audio-visual speech processing: applied to text-dependent speaker recognition

In this paper, an in-depth analysis is undertaken into effective strategies for integrating the audio-visual speech modalities with respect to two major questions. Firstly, at what level should integration occur? Secondly, given a level of integration how should this integration be implemented? Our work is based around the well-known hidden Markov model (HMM) classifier framework for modeling speech. A novel framework for modeling the mismatch between train and test observation sets is proposed, so as to provide effective classifier combination performance between the acoustic and visual HMM classifiers. From this framework, it can be shown that strategies for combining independent classifiers, such as the weighted product or sum rules, naturally emerge depending on the influence of the mismatch. Based on the assumption that poor performance in most audio-visual speech processing applications can be attributed to train/test mismatches we propose that the main impetus of practical audio-visual integration is to dampen the independent errors, resulting from the mismatch, rather than trying to model any bimodal speech dependencies. To this end a strategy is recommended, based on theory and empirical evidence, using a hybrid between the weighted product and weighted sum rules in the presence of varying acoustic noise for the task of text-dependent speaker recognition.
Bispectral analysis of two-dimensional random processes

The extension of bispectral analysis from one-dimensional random processes (e.g. time series) to two-dimensional random processes is presented. Use of the symmetry properties of the bispectrum reduces the number of computations considerably. Numerical simulations demonstrate the ability of two-dimensional bispectral estimation to detect quadratic phase coupling between waves traveling in different directions. By windowing the data, bicoherence leakage can be reduced.
Feature Modelling of PCA Difference Vectors for 2D and 3D Face Recognition

This paper examines the the effectiveness of feature modelling to conduct 2D and 3D face recognition. In particular, PCA difference vectors are modelled using Gaussian Mixture Models (GMMs) which describe Intra-Personal (IP) and Extra-Personal (EP) variations. Two classifiers, an IP and IPEP classifier, are formed using these GMMs and their performance is compared to that of the Mahalanobis cosine metric (MahCosine). The best results for the 2D and 3D face modalities are obtained with the IP and IPEP classifiers respectively. The multi-modal fusion of these two systems provided consistent performance improvement across the FRGC database v2.0.
A suitability metric for mouth tracking through chromatic segmentation

Recently, the use of chromatic segmentation has come very much into vogue for mouth tracking. Our recent work has endeavored to show under what conditions and representations chromatic segmentation works. Results are presented showing that, for some members of the population, chromatic segmentation does not work satisfactorily, irrespective of recording conditions. A suitability metric is proposed that can give a quantitative measure on how well chromatic mouth tracking will work for a given subject.
Motor task event detection using Subthalamic Nucleus Local Field Potentials

Deep Brain Stimulation (DBS) provides significant therapeutic benefit for movement disorders such as Parkinson's disease. Current DBS devices lack real-time feedback (thus are open loop) and stimulation parameters are adjusted during scheduled visits with a clinician. A closed-loop DBS system may reduce power consumption and DBS side effects. In such systems, DBS parameters are adjusted based on patient's behavior, which means that behavior detection is a major step in designing such systems. Various physiological signals can be used to recognize the behaviors. Subthalamic Nucleus (STN) Local Field Potential (LFP) is a great candidate signal for the neural feedback, because it can be recorded from the stimulation lead and does not require additional sensors. A practical behavior detection method should be able to detect behaviors asynchronously meaning that it should not use any prior knowledge of behavior onsets. In this paper, we introduce a behavior detection method that is able to asynchronously detect the finger movements of Parkinson patients. As a result of this study, we learned that there is a motor-modulated inter-hemispheric connectivity between LFP signals recorded bilaterally from STN. We used non-linear regression method to measure this connectivity and use it to detect the finger movements. Performance of this method is evaluated using Receiver Operating Characteristic (ROC).
Head Movement Dynamics during Normal and Perturbed Parent-Infant Interaction

We investigated the dynamics of head motion in parents and infants during an age-appropriate, well-validated emotion induction, the Face-to-Face/Still-Face procedure. Participants were 12 ethnically diverse 6-month-old infants and their mother or father. During infant gaze toward the parent, infant angular amplitude and velocity of pitch and yaw decreased from face-to-face (FF) to still-face (SF) episodes and remained lower in the following Reunion (RE). During infant gaze away from the parent, angular velocity of pitch decreased from FF to SF and remained lower in the RE. Windowed cross-correlation suggested strong bidirectional effects with frequent shifts in the direction of influence. The number of significant positive and negative peaks was higher during FF than RE. Gaze toward and away from the parent was modestly predicted by head orientation. Together, these findings suggest that head motion is strongly related to age-appropriate emotion challenge, are consistent with the hypothesis that perturbations of normal responsiveness carry-over even after the parent resumes normal responsiveness in the reunion, and that there are frequent changes in direction of influence in the postural domain.
Facial action unit recognition with sparse representation

This paper presents a novel framework for recognition of facial action unit (AU) combinations by viewing the classification as a sparse representation problem. Based on this framework, we represent a facial image exhibiting the combination of AUs as a sparse linear combination of basis constituting an overcomplete dictionary. We build an overcomplete dictionary whose main elements are mean Gabor features of AU combinations under examination. The other elements of the dictionary are randomly sampled from a distribution (e.g., Gaussian distribution) that guarantees sparse signal recovery. Afterwards, by solving L1-norm minimization, a facial image is represented as a sparse vector which is used to distinguish various AU patterns. After calculating the sparse representation, the classification problem is simply viewed as a rank maximal problem. The index of the maximal value of the sparse vector is regarded as the class label of the facial image under test. Extensive experiments on the Cohn-Kanade facial expressions database demonstrate that this sparse learning framework is promising for recognition of AU combinations.
Spatio-Temporal Facial Expression Recognition Using Convolutional Neural Networks and Conditional Random Fields

Automated Facial Expression Recognition (FER) has been a challenging task for decades. Many of the existing works use hand-crafted features such as LBP, HOG, LPQ, and Histogram of Optical Flow (HOF) combined with classifiers such as Support Vector Machines for expression recognition. These methods often require rigorous hyperparameter tuning to achieve good results. Recently Deep Neural Networks (DNN) have shown to outperform traditional methods in visual object recognition. In this paper, we propose a two-part network consisting of a DNN-based architecture followed by a Conditional Random Field (CRF) module for facial expression recognition in videos. The first part captures the spatial relation within facial images using convolutional layers followed by three Inception- ResNet modules and two fully-connected layers. To capture the temporal relation between the image frames, we use linear chain CRF in the second part of our network. We evaluate our proposed network on three publicly available databases, viz. CK+, MMI, and FERA. Experiments are performed in subjectindependent and cross-database manners. Our experimental results show that cascading the deep network architecture with the CRF module considerably increases the recognition of facial expressions in videos and in particular it outperforms the stateof- the-art methods in the cross-database experiments and yields comparable results in the subject-independent experiments.
3D Face Recognition Based on 3D Ridge Lines in Range Data

In this paper we present an approach for 3D face recognition from range data based on the principal curvature, kmax, and Hausdorff distance. We use the principal curvature, kmax, to represent the face image as a 3D binary image called ridge image. The ridge image shows the locations of the ridge lines around the important facial regions on the face (i.e. the eyes, the nose, and the mouth). We utilize Hausdorff distance to match the ridge image of a given probe to the created ridge images of the subjects in the gallery. For pose alignment, we extract the locations of three feature points, the inner corners of the two eyes and the tip of the nose using Gaussian curvature. These three feature points plus an auxiliary point in the center of the triangle, made by averaging the coordinates of the three feature points, are used for initial 3D face alignment. In the face recognition stage, we find the optimum pose alignment between the probe image and the gallery, which gives the minimum Hasusdorff distance between the two sets of features. This approach is used for identification of both neutral faces and faces with smile expression. Experiments on a public face database of 61 subjects resulted in 93.5% ranked one recognition rate for neutral expression and 82.0% for the faces with smile expression.
A framework for automated measurement of the intensity of non-posed Facial Action Units

This paper presents a framework to automatically measure the intensity of naturally occurring facial actions. Naturalistic expressions are non-posed spontaneous actions. The facial action coding system (FACS) is the gold standard technique for describing facial expressions, which are parsed as comprehensive, nonoverlapping action units (Aus). AUs have intensities ranging from absent to maximal on a six-point metric (i.e., 0 to 5). Despite the efforts in recognizing the presence of non-posed action units, measuring their intensity has not been studied comprehensively. In this paper, we develop a framework to measure the intensity of AU12 (lip corner puller) and AU6 (cheek raising) in videos captured from infant-mother live face-to-face communications. The AU12 and AU6 are the most challenging case of infant's expressions (e.g., low facial texture in infant's face). One of the problems in facial image analysis is the large dimensionality of the visual data. Our approach for solving this problem is to utilize the spectral regression technique to project high dimensionality facial images into a low dimensionality space. Represented facial images in the low dimensional space are utilized to train support vector machine classifiers to predict the intensity of action units. Analysis of 18 minutes of captured video of non-posed facial expressions of several infants and mothers shows significant agreement between a human FACS coder and our approach, which makes it an efficient approach for automated measurement of the intensity of non-posed facial action units.