Facial affect "in the wild": a survey and a new database

Well-established databases and benchmarks have been developed in the past 20 years for automatic facial behaviour analysis. Nevertheless, for some important problems regarding analysis of facial behaviour, such as (a) estimation of affect in a continuous dimensional space (e.g., valence and arousal) in videos displaying spontaneous facial behaviour and (b) detection of the activated facial muscles (i.e., facial action unit detection), to the best of our knowledge, well-established in-the-wild databases and benchmarks do not exist. That is, the majority of the publicly available corpora for the above tasks contain samples that have been captured in controlled recording conditions and/or captured under a very specific milieu. Arguably, in order to make further progress in automatic understanding of facial behaviour, datasets that have been captured in in the-wild and in various milieus have to be developed. In this paper, we survey the progress that has been recently made on understanding facial behaviour in-the-wild, the datasets that have been developed so far and the methodologies that have been developed, paying particular attention to deep learning techniques for the task. Finally, we make a significant step further and propose a new comprehensive benchmark for training methodologies, as well as assessing the performance of facial affect/behaviour analysis/ understanding in-the-wild. To the best of our knowledge, this is the first time that such a benchmark for valence and arousal "in-the-wild" is presente

Combining Interaction Design and Gaming Technologies for the Development of Interactive Archaeological Content Presentation Systems

Our main objective is to produce state-of-the-art edutainment and serious game end-systems, which satisfy the requirements of all three parties involved in the development process: content experts, end-users and application developers. Their requirements are often cross disciplinary, as each party involved in the process requires solutions to a number of problems which need to be answered in a systematic and complete manner. The ultimate goal of this process is to introduce an efficient, extendable and aesthetically pleasing end-system. In order to achieve these goals, we address and attempt to resolve the most common presentation design issues that arise during the process of interaction design. Completion of this process enables the actual system development to commence with a precise and complete specification of content features and system characteristics

Aff-Wild: Valence and Arousal ‘in-the-wild’ Challenge

The Affect-in-the-Wild (Aff-Wild) Challenge proposes a new comprehensive benchmark for assessing the performance of facial affect/behaviour analysis/understanding 'in-the-wild'. The Aff-wild benchmark contains about 300 videos (over 2,000 minutes of data) annotated with regards to valence and arousal, all captured 'in-the-wild' (the main source being Youtube videos). The paper presents the database description, the experimental set up, the baseline method used for the Challenge and finally the summary of the performance of the different methods submitted to the Affect-in-the-Wild Challenge for Valence and Arousal estimation. The challenge demonstrates that meticulously designed deep neural networks can achieve very good performance when trained with in-the-wild data

Deep affect prediction in-the-wild: Aff-wild database and challenge, deep architectures, and beyond

Automatic understanding of human affect using visual signals is of great importance in everyday human–machine interac- tions. Appraising human emotional states, behaviors and reactions displayed in real-world settings, can be accomplished using latent continuous dimensions (e.g., the circumplex model of affect). Valence (i.e., how positive or negative is an emo- tion) and arousal (i.e., power of the activation of the emotion) constitute popular and effective representations for affect. Nevertheless, the majority of collected datasets this far, although containing naturalistic emotional states, have been captured in highly controlled recording conditions. In this paper, we introduce the Aff-Wild benchmark for training and evaluating affect recognition algorithms. We also report on the results of the First Affect-in-the-wild Challenge (Aff-Wild Challenge) that was recently organized in conjunction with CVPR 2017 on the Aff-Wild database, and was the first ever challenge on the estimation of valence and arousal in-the-wild. Furthermore, we design and extensively train an end-to-end deep neural architecture which performs prediction of continuous emotion dimensions based on visual cues. The proposed deep learning architecture, AffWildNet, includes convolutional and recurrent neural network layers, exploiting the invariant properties of convolutional features, while also modeling temporal dynamics that arise in human behavior via the recurrent layers. The AffWildNet produced state-of-the-art results on the Aff-Wild Challenge. We then exploit the AffWild database for learning features, which can be used as priors for achieving best performances both for dimensional, as well as categorical emo- tion recognition, using the RECOLA, AFEW-VA and EmotiW 2017 datasets, compared to all other methods designed for the same goal. The database and emotion recognition models are available at http://ibug.doc.ic.ac.uk/resources/first-affect-wild-challenge

Predicting the solvation of organic compounds in aqueous environments: from alkanes and alcohols to pharmaceuticals

The development of accurate models to predict the solvation, solubility, and partitioning of nonpolar and amphiphilic compounds in aqueous environments remains an important challenge. We develop state-of-the-art group-interaction models that deliver an accurate description of the thermodynamic properties of alkanes and alcohols in aqueous solution. The group-contribution formulation of the statistical associating fluid theory based on potentials with a variable Mie form (SAFT-γ Mie) is shown to provide accurate predictions of the phase equilibria, including liquid–liquid equilibria, solubility, free energies of solvation, and other infinite-dilution properties. The transferability of the model is further exemplified with predictions of octanol–water partitioning and solubility for a range of organic and pharmaceutically relevant compounds. Our SAFT-γ Mie platform is reliable for the prediction of challenging properties such as mutual solubilities of water and organic compounds which can span over 10 orders of magnitude, while remaining generic in its applicability to a wide range of compounds and thermodynamic conditions. Our work sheds light on contradictory findings related to alkane–water solubility data and the suitability of models that do not account explicitly for polarity

Facial Affect ``in-the-wild": A survey and a new database

Well-established databases and benchmarks have been developed in the past 20 years for automatic facial behaviour analysis. Nevertheless, for some important problems regarding analysis of facial behaviour, such as (a) estimation of affect in a continuous dimensional space (e.g., valence and arousal) in videos displaying spontaneous facial behaviour and (b) detection of the activated facial muscles (i.e., facial action unit detection), to the best of our knowledge, well-established in-the-wild databases and benchmarks do not exist. That is, the majority of the publicly available corpora for the above tasks contain samples that have been captured in controlled recording conditions and/or captured under a very specific milieu. Arguably, in order to make further progress in automatic understanding of facial behaviour, datasets that have been captured in in-the-wild and in various milieus have to be developed. In this paper, we survey the progress that has been recently made on understanding facial behaviour inthe-wild, namely the datasets and methodologies that have been developed thus far, while paying particular attention to recently proposed deep learning techniques. Finally, we attempt a significant step further by proposing a novel, comprehensive benchmark that can be utilized for evaluating and training various methodologies for the problems of facial affect, behaviour analysis and understanding ”inthe-wild”. To the best of our knowledge, this is the first benchmark proposed for measuring continuous affect in the valence-arousal space ”in-the-wild”

Home-based maintenance tele-rehabilitation reduces the risk for acute exacerbations of COPD, hospitalisations and emergency department visits

Pulmonary rehabilitation (PR) remains grossly underutilised by suitable patients worldwide. We investigated whether home-based maintenance tele-rehabilitation will be as effective as hospital-based maintenance rehabilitation and superior to usual care in reducing the risk for acute chronic obstructive pulmonary disease (COPD) exacerbations, hospitalisations and emergency department (ED) visits. Following completion of an initial 2-month PR programme this prospective, randomised controlled trial (between December 2013 and July 2015) compared 12 months of home-based maintenance tele-rehabilitation (n=47) with 12 months of hospital-based, outpatient, maintenance rehabilitation (n=50) and also to 12 months of usual care treatment (n=50) without initial PR. In a multivariate analysis during the 12-month follow-up, both home-based tele-rehabilitation and hospital-based PR remained independent predictors of a lower risk for 1) acute COPD exacerbation (incidence rate ratio (IRR) 0.517, 95% CI 0.389–0.687, and IRR 0.635, 95% CI 0.473–0.853), respectively, and 2) hospitalisations for acute COPD exacerbation (IRR 0.189, 95% CI 0.100–0.358, and IRR 0.375, 95% CI 0.207–0.681), respectively. However, only home-based maintenance tele-rehabilitation and not hospital-based, outpatient, maintenance PR was an independent predictor of ED visits (IRR 0.116, 95% CI 0.072–0.185). Home-based maintenance tele-rehabilitation is equally effective as hospital-based, outpatient, maintenance PR in reducing the risk for acute COPD exacerbation and hospitalisations. In addition, it encounters a lower risk for ED visits, thereby constituting a potentially effective alternative strategy to hospital-based, outpatient, maintenance PR