Facial expressions depicting compassionate and critical emotions: the development and validation of a new emotional face stimulus set

Attachment with altruistic others requires the ability to appropriately process affiliative and kind facial cues. Yet there is no stimulus set available to investigate such processes. Here, we developed a stimulus set depicting compassionate and critical facial expressions, and validated its effectiveness using well-established visual-probe methodology. In Study 1, 62 participants rated photographs of actors displaying compassionate/kind and critical faces on strength of emotion type. This produced a new stimulus set based on N = 31 actors, whose facial expressions were reliably distinguished as compassionate, critical and neutral. In Study 2, 70 participants completed a visual-probe task measuring attentional orientation to critical and compassionate/kind faces. This revealed that participants lower in self-criticism demonstrated enhanced attention to compassionate/kind faces whereas those higher in self-criticism showed no bias. To sum, the new stimulus set produced interpretable findings using visual-probe methodology and is the first to include higher order, complex positive affect displays

Machine Understanding of Human Behavior

A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. If this prediction is to come true, then next generation computing, which we will call human computing, should be about anticipatory user interfaces that should be human-centered, built for humans based on human models. They should transcend the traditional keyboard and mouse to include natural, human-like interactive functions including understanding and emulating certain human behaviors such as affective and social signaling. This article discusses a number of components of human behavior, how they might be integrated into computers, and how far we are from realizing the front end of human computing, that is, how far are we from enabling computers to understand human behavior

Affective games:a multimodal classification system

Affective gaming is a relatively new field of research that exploits human emotions to influence gameplay for an enhanced player experience. Changes in player’s psychology reflect on their behaviour and physiology, hence recognition of such variation is a core element in affective games. Complementary sources of affect offer more reliable recognition, especially in contexts where one modality is partial or unavailable. As a multimodal recognition system, affect-aware games are subject to the practical difficulties met by traditional trained classifiers. In addition, inherited game-related challenges in terms of data collection and performance arise while attempting to sustain an acceptable level of immersion. Most existing scenarios employ sensors that offer limited freedom of movement resulting in less realistic experiences. Recent advances now offer technology that allows players to communicate more freely and naturally with the game, and furthermore, control it without the use of input devices. However, the affective game industry is still in its infancy and definitely needs to catch up with the current life-like level of adaptation provided by graphics and animation

Machine Analysis of Facial Expressions

No abstract

Contrasting Experimentally Device-Manipulated and Device-Free Smiles.

Researchers in psychology have long been interested in not only studying smiles, but in examining the downstream effects of experimentally manipulated smiles. To experimentally manipulate smiles unobtrusively, participants typically hold devices (e.g., pens or chopsticks) in their mouths in a manner that activates the muscles involved in smiling. Surprisingly, despite decades of research using these methods, no study has tested to what degree these methods activate the same muscles as more natural, device-free smiles. Our study fills this gap in the literature by contrasting the magnitude of muscle activation in device-free smiles against the popular chopstick/pen manipulation. We also contrast these methods against the Smile Stick, a new device specifically designed to manipulate smiles in a comfortable and hygienic fashion. One hundred fifty-nine participants each participated in three facial expression manipulations that were held for 1 min: smile manipulation via Smile Stick, smile manipulation via chopsticks, and device-free smile. Facial electromyography was used to measure the intensity of the activation of the two main types of muscles involved in genuine, Duchenne smiling: the orbicularis oculi (a muscle group around the eyes) and the zygomaticus major (a muscle group in the cheeks). Furthermore, following each manipulation, participants rated their experience of the manipulation (i.e., comfort, fatigue, and difficulty), experienced affect (positive and negative), and levels of arousal. Results indicated that the Smile Stick and chopsticks performed equally across all measurements. Device-free smiles were rated as most comfortable but also the most fatiguing, and procured the greatest levels of positive affect and lowest levels of negative affect. Furthermore, device-free smiles resulted in significantly higher levels of both zygomaticus major (by ∼40%) and orbicularis oculi (by ∼15%) muscle activation than either the Smile Stick or chopsticks. The two devices were not different from each other in muscle activation. This study reveals that while device-free smiling procures the greatest changes in muscle activation and affect change, smiling muscle groups are activated by device manipulations, and expected changes in affect do occur, albeit to a lesser degree than device-free smiling. It also indicates that the Smile Stick is an acceptable and comparable alternative to disposable chopsticks

Sistema de reconhecimento de expressões faciais para deteção de stress

Stress is the body's natural reaction to external and internal stimuli. Despite being something natural, prolonged exposure to stressors can contribute to serious health problems. These reactions are reflected not only physiologically, but also psychologically, translating into emotions and facial expressions. Once this relationship between the experience of stressful situations and the demonstration of certain emotions in response was understood, it was decided to develop a system capable of classifying facial expressions and thereby creating a stress detector. The proposed solution consists of two main blocks. A convolutional neural network capable of classifying facial expressions, and an application that uses this model to classify real-time images of the user's face and thereby verify whether or not it shows signs of stress. The application consists in capturing real-time images from the webcam, extract the user's face, classify which facial expression he expresses, and with these classifications assess whether or not he shows signs of stress in a given time interval. As soon as the application determines the presence of signs of stress, it notifies the user. For the creation of the classification model, was used transfer learning, together with finetuning. In this way, we took advantage of the pre-trained networks VGG16, VGG19, and Inception-ResNet V2 to solve the problem at hand. For the transfer learning process, were also tried two classifier architectures. After several experiments, it was determined that VGG16, together with a classifier made up of a convolutional layer, was the candidate with the best performance at classifying stressful emotions. Having presented an MCC of 0.8969 in the test images of the KDEF dataset, 0.5551 in the Net Images dataset, and 0.4250 in the CK +.O stress é uma reação natural do corpo a estímulos externos e internos. Apesar de ser algo natural, a exposição prolongada a stressors pode contribuir para sérios problemas de saúde. Essas reações refletem-se não só fisiologicamente, mas também psicologicamente. Traduzindose em emoções e expressões faciais. Uma vez compreendida esta relação entre a experiência de situações stressantes e a demonstração de determinadas emoções como resposta, decidiu-se desenvolver um sistema capaz de classificar expressões faciais e com isso criar um detetor de stress. A solução proposta é constituida por dois blocos fundamentais. Uma rede neuronal convolucional capaz de classificar expressões faciais e uma aplicação que utiliza esse modelo para classificar imagens em tempo real do rosto do utilizador e assim averiguar se este apresenta ou não sinais de stress. A aplicação consiste em captar imagens em tempo real a partir da webcam, extrair o rosto do utilizador, classificar qual a expressão facial que este manifesta, e com essas classificações avaliar se num determinado intervalo temporal este apresenta ou não sinais de stress. Assim que a aplicação determine a presença de sinais de stress, esta irá notificar o utilizador. Para a criação do modelo de classificação, foi utilizado transfer learning, juntamente com finetuning. Desta forma tirou-se partido das redes pre-treinadas VGG16, VGG19, e InceptionResNet V2 para a resolução do problema em mãos. Para o processo de transfer learning foram também experimentadas duas arquiteturas de classificadores. Após várias experiências, determinou-se que a VGG16, juntamente com um classificador constituido por uma camada convolucional era a candidata com melhor desempenho a classificar emoções stressantes. Tendo apresentado um MCC de 0,8969 nas imagens de teste do conjunto de dados KDEF, 0,5551 no conjunto de dados Net Images, e 0,4250 no CK+

Differential relationships of childhood trauma and violent behaviour in adolescents with cognitive-emotional deficits

Konvergierende neurobiologische Studien zeigen, dass Negative Kindheitserfahrungen (Adverse Childhood Experiences, ACEs) mehrere dicht miteinander verknüpfte Hirnsysteme beeinflussen und die Entwicklung von Exekutivfunktionen stören, insbesondere die Fähigkeit, unangemessene Affekte und Handlungen zu unterdrücken. In der vorliegenden Arbeit wurde zunächst untersucht, wie Exekutivfunktionen in nicht-emotionalen (kühlen) und emotionalen (heißen) Situationen (d. h. Emotionsregulation) mit einer hohen Exposition gegenüber ACEs zusammenhängen und ob solche Beziehungen durch akuten Stress verstärkt werden. Unsere Ergebnisse zeigen, dass gewalttätiges Verhalten bei Opfern von Kindheitstraumata eher mit Defiziten in heißen Exekutivfunktionen als mit Defiziten in kühlen Exekutivfunktionen verbunden sein könnte, insbesondere unter Stressbedingungen. Insgesamt zeigt diese Studie erstens die Nützlichkeit von OpenFace, einem kostengünstigen und dennoch effektiven Instrument zur Untersuchung des mimischen Verhaltens bei der Emotionsregulation. Zweitens eröffnet sie Perspektiven für eine gezieltere Erforschung von und Interventionen bei ACEs. Drittens bezieht sie Jugendliche ein, eine wenig erforschte Altersgruppe, die sich in einer sensiblen Phase der Entwicklung von Exekutivfunktionen befinden.Converging neurobiological studies show that Adverse Childhood Experiences (ACEs) affect multiple densely interconnected neurobiological systems and disrupt the development of executive functions (EFs), especially the ability to inhibit inappropriate affects and actions, potentially modulating factors in the relationship between ACEs and violent behaviour. This study first sought to assess how EFs under non-emotional (cool) and emotional (hot) situations (i.e. emotion regulation) are related with high-exposure to ACEs, and whether any such relationships would be aggravated by acute stress. Our findings show that violent behavior among victims of childhood trauma might be associated more with deficits in hot EFs than it is with deficits in cool EFs, especially more so under conditions of stress. Altogether, this study first shows the usefulness of OpenFace, a low cost yet effective tool to study facial behaviour in emotion regulation. Second, it opens perspectives towards more targeted research on, and interventions for ACEs, and third, it involves adolescents, a little researched age group, yet a sensitive period of EFs development