nils-herrmann/clean_scirepeval huggingface dataset:
An implicit spatiotemporal shape model for human activity localization and recognition

In this paper we address the problem of localisation and recognition of human activities in unsegmented image sequences. The main contribution of the proposed method is the use of an implicit representation of the spatiotemporal shape of the activity which relies on the spatiotemporal localization of characteristic, sparse, `visual words' and `visual verbs'. Evidence for the spatiotemporal localization of the activity are accumulated in a probabilistic spatiotemporal voting scheme. The local nature of our voting framework allows us to recover multiple activities that take place in the same scene, as well as activities in the presence of clutter and occlusions. We construct class-specific codebooks using the descriptors in the training set, where we take the spatial co-occurrences of pairs of codewords into account. The positions of the codeword pairs with respect to the object centre, as well as the frame in the training set in which they occur are subsequently stored in order to create a spatiotemporal model of codeword co-occurrences. During the testing phase, we use mean shift mode estimation in order to spatially segment the subject that performs the activities in every frame, and the Radon transform in order to extract the most probable hypotheses concerning the temporal segmentation of the activities within the continuous stream.
Fast Newton active appearance models

Active Appearance Models (AAMs) are statistical models of shape and appearance widely used in computer vision to detect landmarks on objects like faces. Fitting an AAM to a new image can be formulated as a non-linear least-squares problem which is typically solved using iterative methods. Owing to its efficiency, Gauss-Newton optimization has been the standard choice over more sophisticated approaches like Newton. In this paper, we show that the AAM problem has structure which can be used to solve efficiently the original Newton problem without any approximations. We then make connections to the original Gauss-Newton algorithm and study experimentally the effect of the additional terms introduced by the Newton formulation on both fitting accuracy and convergence. Based on our derivations, we also propose a combined Newton and Gauss-Newton method which achieves promising fitting and convergence performance. Our findings are validated on two challenging in-the-wild data sets.
Facial action unit recognition using temporal templates

Automatic recognition of human facial expressions is a challenging problem with many applications in human-computer interaction. Most of the existing facial expression analyzers succeed only in recognizing a few emotional facial expressions, such as anger or happiness. Instead of being another approach to automatic detection of prototypic facial expressions of emotion, this work attempts to measure a large range of facial behavior by recognizing facial action units (AUs, i.e. atomic facial signals) that produce expressions. The proposed system performs AU recognition using temporal templates as input data. Temporal templates are 2D images, constructed from image sequences, which show where and when motion in the image sequence has occurred. A two-stage learning machine, combining a k-nearest-neighbor (kNN) algorithm and a rule-based system, performs the recognition of 15 AUs occurring alone or in combination in an input face image sequence. Each rule utilized for recognition of a given AU (or a given AU combination) is based on the presence of a specific temporal template in a particular facial region, in which the presence of facial muscle activity characterizes the AU (or AU combination) in question. When trained and tested on the Cohn-Kanade face image database, the proposed method achieved an average recognition rate of 76.2%.
Implicit human-centered tagging [Social Sciences]

Tagging is the annotation of multimedia data with userspecified keywords known as tags, with the aim of facilitating fast and accurate data retrieval based on these tags. In contrast to this process, also referred to as explicit tagging, implicit human-centered tagging (IHCT) refers to exploiting the information on user's nonverbal reactions (e.g., facial expressions like smiles or head gestures like shakes) to multimedia data, with which he or she interacts, to assign new or improve the existing tags associated with the target data. Thus, implicit tagging allows that a data item gets tagged each time a user interacts with it based on the reactions of the user to the data (e.g., laughter when seeing a funny video), in contrast to explicit tagging paradigm in which a data item gets tagged only if a user is requested (or chooses) to associate tags with it. As nonverbal reactions to observed multimedia are displayed naturally and spontaneously, no purposeful explicit action (effort) is required from the user; hence, the resulting tagging process is said to be "implicit" and "human centered" (in contrast to being dictated by computer and being "computer-centered").
Facial action detection from dual-view static face images

This work presents an automatic system that we developed for automatic recognition of facial gestures (facial muscle activity) from static images of combined frontal-and profile-view of the face. For the frontal view, the face region is subjected to multi-detector processing which per facial component (eyes, eyebrows, mouth), generates a spatial sample of its contour. A set of 19 frontal-face feature points is then extracted from the spatially sampled contours of the facial features. For the profile view, 10 feature points are extracted from the contour of the face-profile region. Based on these 29 points, 29 individual facial muscle action units (AUs) occurring alone or in combinations in an input dual-view image are recognized using a rule-based reasoning. With each scored AU, the utilized algorithm associates a factor denoting the certainty with which the pertinent AU has been scored. A recognition rate of 86% is achieved.
Coupled Gaussian processes for pose-invariant facial expression recognition

We propose a method for head-pose invariant facial expression recognition that is based on a set of characteristic facial points. To achieve head-pose invariance, we propose the Coupled Scaled Gaussian Process Regression (CSGPR) model for head-pose normalization. In this model, we first learn independently the mappings between the facial points in each pair of (discrete) nonfrontal poses and the frontal pose, and then perform their coupling in order to capture dependences between them. During inference, the outputs of the coupled functions from different poses are combined using a gating function, devised based on the head-pose estimation for the query points. The proposed model outperforms state-of-the-art regression-based approaches to head-pose normalization, 2D and 3D Point Distribution Models (PDMs), and Active Appearance Models (AAMs), especially in cases of unknown poses and imbalanced training data. To the best of our knowledge, the proposed method is the first one that is able to deal with expressive faces in the range from $(-45^\circ)$ to $(+45^\circ)$ pan rotation and $(-30^\circ)$ to $(+30^\circ)$ tilt rotation, and with continuous changes in head pose, despite the fact that training was conducted on a small set of discrete poses. We evaluate the proposed method on synthetic and real images depicting acted and spontaneously displayed facial expressions.
Prediction-Based Audiovisual Fusion for Classification of Non-Linguistic Vocalisations

Prediction plays a key role in recent computational models of the brain and it has been suggested that the brain constantly makes multisensory spatiotemporal predictions. Inspired by these findings we tackle the problem of audiovisual fusion from a new perspective based on prediction. We train predictive models which model the spatiotemporal relationship between audio and visual features by learning the audio-to-visual and visual-to-audio feature mapping for each class. Similarly, we train predictive models which model the time evolution of audio and visual features by learning the past-to-future feature mapping for each class. In classification, all the class-specific regression models produce a prediction of the expected audio/visual features and their prediction errors are combined for each class. The set of class-specific regressors which best describes the audiovisual feature relationship, i.e., results in the lowest prediction error, is chosen to label the input frame. We perform cross-database experiments, using the AMI, SAL, and MAHNOB databases, in order to classify laughter and speech and subject-independent experiments on the AVIC database in order to classify laughter, hesitation and consent. In virtually all cases prediction-based audiovisual fusion consistently outperforms the two most commonly used fusion approaches, decision-level and feature-level fusion.
Spatiotemporal Localization and Categorization of Human Actions in Unsegmented Image Sequences

In this paper we address the problem of localization and recognition of human activities in unsegmented image sequences. The main contribution of the proposed method is the use of an implicit representation of the spatiotemporal shape of the activity which relies on the spatiotemporal localization of characteristic ensembles of feature descriptors. Evidence for the spatiotemporal localization of the activity is accumulated in a probabilistic spatiotemporal voting scheme. The local nature of the proposed voting framework allows us to deal with multiple activities taking place in the same scene, as well as with activities in the presence of clutter and occlusion. We use boosting in order to select characteristic ensembles per class. This leads to a set of class specific codebooks where each codeword is an ensemble of features. During training, we store the spatial positions of the codeword ensembles with respect to a set of reference points, as well as their temporal positions with respect to the start and end of the action instance. During testing, each activated codeword ensemble casts votes concerning the spatiotemporal position and extend of the action, using the information that was stored during training. Mean Shift mode estimation in the voting space provides the most probable hypotheses concerning the localization of the subjects at each frame, as well as the extend of the activities depicted in the image sequences. We present classification and localization results for a number of publicly available datasets, and for a number of sequences where there is a significant amount of clutter and occlusion.
Neural conditional ordinal random fields for agreement level estimation

We present a novel approach to automated estimation of agreement intensity levels from facial images. To this end, we employ the MAHNOB Mimicry database of subjects recorded during dyadic interactions, where the facial images are annotated in terms of agreement intensity levels using the Likert scale (strong disagreement, disagreement, neutral, agreement and strong agreement). Dynamic modelling of the agreement levels is accomplished by means of a Conditional Ordinal Random Field model. Specifically, we propose a novel Neural Conditional Ordinal Random Field model that performs non-linear feature extraction from face images using the notion of Neural Networks, while also modelling temporal and ordinal relationships between the agreement levels. We show in our experiments that the proposed approach outperforms existing methods for modelling of sequential data. The preliminary results obtained on five subjects demonstrate that the intensity of agreement can successfully be estimated from facial images (39% F1 score) using the proposed method.
Visual-only discrimination between native and non-native speech

Accent is an important biometric characteristic that is defined by the presence of specific traits in the speaking style of an individual. These are identified by patterns in the speech production system, such as those present in the vocal tract or in lip movements. Evidence from linguistics and speech processing research suggests that visual information enhances speech recognition. Intrigued by these findings, along with the assumption that visually perceivable accent-related patterns are transferred from the mother tongue to a foreign language, we investigate the task of discriminating native from non-native speech in English, employing visual features only. Training and evaluation is performed on segments of continuous visual speech, captured by mobile phones, where all speakers read the same text. We apply various appearance descriptors to represent the mouth region at each video frame. Vocabulary-based histograms, being the final representation of dynamic features for all utterances, are used for recognition. Binary classification experiments, discriminating native and non-native speakers, are conducted in a subject-independent manner. Our results show that this task can be addressed by means of an automated approach that uses visual features only.
Facial point detection using boosted regression and graph models

Finding fiducial facial points in any frame of a video showing rich naturalistic facial behaviour is an unsolved problem. Yet this is a crucial step for geometric-feature-based facial expression analysis, and methods that use appearance-based features extracted at fiducial facial point locations. In this paper we present a method based on a combination of Support Vector Regression and Markov Random Fields to drastically reduce the time needed to search for a point's location and increase the accuracy and robustness of the algorithm. Using Markov Random Fields allows us to constrain the search space by exploiting the constellations that facial points can form. The regressors on the other hand learn a mapping between the appearance of the area surrounding a point and the positions of these points, which makes detection of the points very fast and can make the algorithm robust to variations of appearance due to facial expression and moderate changes in head pose. The proposed point detection algorithm was tested on 1855 images, the results of which showed we outperform current state of the art point detectors.
Doubly Sparse Relevance Vector Machine for Continuous Facial Behavior Estimation

Certain inner feelings and physiological states like pain are subjective states that cannot be directly measured, but can be estimated from spontaneous facial expressions. Since they are typically characterized by subtle movements of facial parts, analysis of the facial details is required. To this end, we formulate a new regression method for continuous estimation of the intensity of facial behavior interpretation, called Doubly Sparse Relevance Vector Machine (DSRVM). DSRVM enforces double sparsity by jointly selecting the most relevant training examples (a.k.a. relevance vectors) and the most important kernels associated with facial parts relevant for interpretation of observed facial expressions. This advances prior work on multi-kernel learning, where sparsity of relevant kernels is typically ignored. Empirical evaluation on challenging Shoulder Pain videos, and the benchmark DISFA and SEMAINE datasets demonstrate that DSRVM outperforms competing approaches with a multi-fold reduction of running times in training and testing.
Temporal Archetypal Analysis for Action Segmentation

Unsupervised learning of invariant representations that efficiently describe high-dimensional time series has several applications in dynamic visual data analysis. Clearly, the problem becomes more challenging when dealing with multiple time series arising from different modalities. A prominent example of this multimodal setting is the human motion which can be represented by multimodal time series of pixel intensities, depth maps, and motion capture data. Here, we study, for the first time, the problem of unsupervised learning of temporally and modality invariant informative representations, referred to as archetypes, from multiple time series originating from different modalities. To this end a novel method, coined as temporal archetypal analysis, is proposed. The performance of the proposed method is assessed by conducting experiments in unsupervised action segmentation. Experimental results on three different real world datasets using single modal and multimodal visual representations indicate the robustness and effectiveness of the proposed methods, outperforming compared state-of-the-art methods by a large, in most of the cases, margin.
Facial action recognition for facial expression analysis from static face images

Automatic recognition of facial gestures (i.e., facial muscle activity) is rapidly becoming an area of intense interest in the research field of machine vision. In this paper, we present an automated system that we developed to recognize facial gestures in static, frontal- and/or profile-view color face images. A multidetector approach to facial feature localization is utilized to spatially sample the profile contour and the contours of the facial components such as the eyes and the mouth. From the extracted contours of the facial features, we extract ten profile-contour fiducial points and 19 fiducial points of the contours of the facial components. Based on these, 32 individual facial muscle actions (AUs) occurring alone or in combination are recognized using rule-based reasoning. With each scored AU, the utilized algorithm associates a factor denoting the certainty with which the pertinent AU has been scored. A recognition rate of 86% is achieved.
Automatic Analysis of Facial Expressions: The State of the Art

Humans detect and interpret faces and facial expressions in a scene with little or no effort. Still, development of an automated system that accomplishes this task is rather difficult. There are several related problems: detection of an image segment as a face, extraction of the facial expression information, and classification of the expression (e.g., in emotion categories). A system that performs these operations accurately and in real time would form a big step in achieving a human-like interaction between man and machine. The paper surveys the past work in solving these problems. The capability of the human visual system with respect to these problems is discussed, too. It is meant to serve as an ultimate goal and a guide for determining recommendations for development of an automatic facial expression analyzer.
String-based audiovisual fusion of behavioural events for the assessment of dimensional affect

The automatic assessment of affect is mostly based on feature-level approaches, such as distances between facial points or prosodic and spectral information when it comes to audiovisual analysis. However, it is known and intuitive that behavioural events such as smiles, head shakes or laughter and sighs also bear highly relevant information regarding a subject's affective display. Accordingly, we propose a novel string-based prediction approach to fuse such events and to predict human affect in a continuous dimensional space. Extensive analysis and evaluation has been conducted using the newly released SEMAINE database of human-to-agent communication. For a thorough understanding of the obtained results, we provide additional benchmarks by more conventional feature-level modelling, and compare these and the string-based approach to fusion of signal-based features and string-based events. Our experimental results show that the proposed string-based approach is the best performing approach for automatic prediction of Valence and Expectation dimensions, and improves prediction performance for the other dimensions when combined with at least acoustic signal-based features.
Fully Automatic Recognition of the Temporal Phases of Facial Actions

Past work on automatic analysis of facial expressions has focused mostly on detecting prototypic expressions of basic emotions like happiness and anger. The method proposed here enables the detection of a much larger range of facial behavior by recognizing facial muscle actions [action units (AUs)] that compound expressions. AUs are agnostic, leaving the inference about conveyed intent to higher order decision making (e.g., emotion recognition). The proposed fully automatic method not only allows the recognition of 22 AUs but also explicitly models their temporal characteristics (i.e., sequences of temporal segments: neutral, onset, apex, and offset). To do so, it uses a facial point detector based on Gabor-feature-based boosted classifiers to automatically localize 20 facial fiducial points. These points are tracked through a sequence of images using a method called particle filtering with factorized likelihoods. To encode AUs and their temporal activation models based on the tracking data, it applies a combination of GentleBoost, support vector machines, and hidden Markov models. We attain an average AU recognition rate of 95.3% when tested on a benchmark set of deliberately displayed facial expressions and 72% when tested on spontaneous expressions.
Active Learning of Introductory Machine Learning

This paper describes a computer-based training program for active learning of agent technology, expert systems, neural networks and case-based reasoning by undergraduate students using a simple agent framework. While many machine learning (ML) and artificial intelligence (AI) courses teach ML and AI concepts by means of programming assignments, these assignments have usually no connection to how the student will apply the newly obtained knowledge to previously unseen, real-world problems. The pedagogy that we adopted here is computer-based active learning: teams of students are presented with well-defined assignments aimed at building intelligent agents for person identification and recognition of facial expressions and emotions from video recordings of their faces. Classroom experience indicates that the students found the specified programming assignments highly motivating. Objective evaluation studies suggest that students learn much more effectively when a contextualized, collaborative, constructive, and reflective approach is used than when an orthodox, objectivist approach to teaching ML and AI techniques is used alone
Output-associative RVM regression for dimensional and continuous emotion prediction

Many problems in machine learning and computer vision consist of predicting multi-dimensional output vectors given a specific set of input features. In many of these problems, there exist inherent temporal and spacial dependencies between the output vectors, as well as repeating output patterns and input-output associations, that can provide more robust and accurate predictors when modelled properly. With this intrinsic motivation, we propose a novel Output-Associative Relevance Vector Machine (OA-RVM) regression framework that augments the traditional RVM regression by being able to learn non-linear input and output dependencies. Instead of depending solely on the input patterns, OA-RVM models output structure and covariances within a predefined temporal window, thus capturing past, current and future context. As a result, output patterns manifested in the training data are captured within a formal probabilistic framework, and subsequently used during inference. As a proof of concept, we target the highly challenging problem of dimensional and continuous prediction of emotions from naturalistic facial expressions. We demonstrate the advantages of the proposed OA-RVM regression by performing both subject-dependent and subject-independent experiments using the SAL database. The experimental results show that OA-RVM regression outperforms the traditional RVM and SVM regression approaches in prediction accuracy, generating more robust and accurate models.