Distinguishing Posed and Spontaneous Smiles by Facial Dynamics

Smile is one of the key elements in identifying emotions and present state of mind of an individual. In this work, we propose a cluster of approaches to classify posed and spontaneous smiles using deep convolutional neural network (CNN) face features, local phase quantization (LPQ), dense optical flow and histogram of gradient (HOG). Eulerian Video Magnification (EVM) is used for micro-expression smile amplification along with three normalization procedures for distinguishing posed and spontaneous smiles. Although the deep CNN face model is trained with large number of face images, HOG features outperforms this model for overall face smile classification task. Using EVM to amplify micro-expressions did not have a significant impact on classification accuracy, while the normalizing facial features improved classification accuracy. Unlike many manual or semi-automatic methodologies, our approach aims to automatically classify all smiles into either `spontaneous' or `posed' categories, by using support vector machines (SVM). Experimental results on large UvA-NEMO smile database show promising results as compared to other relevant methods.Comment: 16 pages, 8 figures, ACCV 2016, Second Workshop on Spontaneous Facial Behavior Analysi

The effect of relationship status on communicating emotions through touch

Research into emotional communication to date has largely focused on facial and vocal expressions. In contrast, recent studies by Hertenstein, Keltner, App, Bulleit, and Jaskolka (2006) and Hertenstein, Holmes, McCullough, and Keltner (2009) exploring nonverbal communication of emotion discovered that people could identify anger, disgust, fear, gratitude, happiness, love, sadness and sympathy from the experience of being touched on either the arm or body by a stranger, without seeing the touch. The study showed that strangers were unable to communicate the self-focused emotions embarrassment, envy and pride, or the universal emotion surprise. Literature relating to touch indicates that the interpretation of a tactile experience is significantly influenced by the relationship between the touchers (Coan, Schaefer, & Davidson, 2006). The present study compared the ability of romantic couples and strangers to communicate emotions solely via touch. Results showed that both strangers and romantic couples were able to communicate universal and prosocial emotions, whereas only romantic couples were able to communicate the self-focused emotions envy and pride

STEPS - an approach for human mobility modeling

In this paper we introduce Spatio-TEmporal Parametric Stepping (STEPS) - a simple parametric mobility model which can cover a large spectrum of human mobility patterns. STEPS makes abstraction of spatio-temporal preferences in human mobility by using a power law to rule the nodes movement. Nodes in STEPS have preferential attachment to favorite locations where they spend most of their time. Via simulations, we show that STEPS is able, not only to express the peer to peer properties such as inter-ontact/contact time and to reflect accurately realistic routing performance, but also to express the structural properties of the underlying interaction graph such as small-world phenomenon. Moreover, STEPS is easy to implement, exible to configure and also theoretically tractable

Global wellposed problem for the 3-D incompressible anisotropic Navier-Stokes equations

In this paper, we consider a global wellposed problem for the 3-D incompressible anisotropic Navier-Stokes equations (\textit{ANS}). In order to do so, we first introduce the scaling invariant Besov-Sobolev type spaces, $B^{-1+\frac{2}{p},{1/2}}_{p}$ and $B^{-1+\frac{2}{p},{1/2}}_{p}(T)$, $p\geq2$. Then, we prove the global wellposedness for (\textit{ANS}) provided the initial data are sufficient small compared to the horizontal viscosity in some suitable sense, which is stronger than $B^{-1+\frac{2}{p},{1/2}}_{p}$ norm. In particular, our results imply the global wellposedness of (\textit{ANS}) with high oscillatory initial data.Comment: 39 page

GANimation: anatomically-aware facial animation from a single image

The final publication is available at link.springer.comRecent advances in Generative Adversarial Networks (GANs) have shown impressive results for task of facial expression synthesis. The most successful architecture is StarGAN, that conditions GANs' generation process with images of a specific domain, namely a set of images of persons sharing the same expression. While effective, this approach can only generate a discrete number of expressions, determined by the content of the dataset. To address this limitation, in this paper, we introduce a novel GAN conditioning scheme based on Action Units (AU) annotations, which describes in a continuous manifold the anatomical facial movements defining a human expression. Our approach allows controlling the magnitude of activation of each AU and combine several of them. Additionally, we propose a fully unsupervised strategy to train the model, that only requires images annotated with their activated AUs, and exploit attention mechanisms that make our network robust to changing backgrounds and lighting conditions. Extensive evaluation show that our approach goes beyond competing conditional generators both in the capability to synthesize a much wider range of expressions ruled by anatomically feasible muscle movements, as in the capacity of dealing with images in the wild.Peer ReviewedAward-winningPostprint (author's final draft

Inverse modelling of cloud-aerosol interactions – Part 2: Sensitivity tests on liquid phase clouds using a Markov chain Monte Carlo based simulation approach

This paper presents a novel approach to investigate cloud-aerosol interactions by coupling a Markov chain Monte Carlo (MCMC) algorithm to an adiabatic cloud parcel model. Despite the number of numerical cloud-aerosol sensitivity studies previously conducted few have used statistical analysis tools to investigate the global sensitivity of a cloud model to input aerosol physiochemical parameters. Using numerically generated cloud droplet number concentration (CDNC) distributions (i.e. synthetic data) as cloud observations, this inverse modelling framework is shown to successfully estimate the correct calibration parameters, and their underlying posterior probability distribution. <br></br> The employed analysis method provides a new, integrative framework to evaluate the global sensitivity of the derived CDNC distribution to the input parameters describing the lognormal properties of the accumulation mode aerosol and the particle chemistry. To a large extent, results from prior studies are confirmed, but the present study also provides some additional insights. There is a transition in relative sensitivity from very clean marine Arctic conditions where the lognormal aerosol parameters representing the accumulation mode aerosol number concentration and mean radius and are found to be most important for determining the CDNC distribution to very polluted continental environments (aerosol concentration in the accumulation mode >1000 cm<sup>−3</sup>) where particle chemistry is more important than both number concentration and size of the accumulation mode. <br></br> The competition and compensation between the cloud model input parameters illustrates that if the soluble mass fraction is reduced, the aerosol number concentration, geometric standard deviation and mean radius of the accumulation mode must increase in order to achieve the same CDNC distribution. <br></br> This study demonstrates that inverse modelling provides a flexible, transparent and integrative method for efficiently exploring cloud-aerosol interactions with respect to parameter sensitivity and correlation

The role of motion and intensity in deaf children’s recognition of real human facial expressions of emotion

© 2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.There is substantial evidence to suggest that deafness is associated with delays in emotion understanding, which has been attributed to delays in language acquisition and opportunities to converse. However, studies addressing the ability to recognise facial expressions of emotion have produced equivocal findings. The two experiments presented here attempt to clarify emotion recognition in deaf children by considering two aspects: the role of motion and the role of intensity in deaf children’s emotion recognition. In Study 1, 26 deaf children were compared to 26 age-matched hearing controls on a computerised facial emotion recognition task involving static and dynamic expressions of 6 emotions. Eighteen of the deaf and 18 age-matched hearing controls additionally took part in Study 2, involving the presentation of the same 6 emotions at varying intensities. Study 1 showed that deaf children’s emotion recognition was better in the dynamic rather than static condition, whereas the hearing children showed no difference in performance between the two conditions. In Study 2, the deaf children performed no differently from the hearing controls, showing improved recognition rates with increasing rates of intensity. With the exception of disgust, no differences in individual emotions were found. These findings highlight the importance of using ecologically valid stimuli to assess emotion recognition.Peer reviewedFinal Published versio

Asymptotic models for the generation of internal waves by a moving ship, and the dead-water phenomenon

This paper deals with the dead-water phenomenon, which occurs when a ship sails in a stratified fluid, and experiences an important drag due to waves below the surface. More generally, we study the generation of internal waves by a disturbance moving at constant speed on top of two layers of fluids of different densities. Starting from the full Euler equations, we present several nonlinear asymptotic models, in the long wave regime. These models are rigorously justified by consistency or convergence results. A careful theoretical and numerical analysis is then provided, in order to predict the behavior of the flow and in which situations the dead-water effect appears.Comment: To appear in Nonlinearit

Attentional bias towards threatening and neutral facial expressions in high trait anxious children.

Research suggests anxious children display increased attentional biases for threat-related stimuli. However, findings based upon spatial domain research are equivocal. Moreover, few studies allow for the independent analysis of trials containing neutral (i.e., potentially ambiguous) faces. Here, we report two temporal attentional blink experiments with high trait anxious (HTA) and low trait anxious (LTA) children. In an emotive experiment, we manipulated the valence of the second target (T2: threatening/positive/neutral). Results revealed that HTA, relative to LTA, children demonstrated better performance on neutral trials. Additionally, HTA children demonstrated a threat-superiority effect whereas LTA children demonstrated an emotion-superiority effect. In a non-emotive control, no differences between HTA and LTA children were observed. Results suggest trait anxiety is associated with an attentional bias for threat in children. Additionally, the neutral face finding suggests HTA children bias attention towards ambiguity. These findings could have important implications for current anxiety disorder research and treatments