Recent theories of emotion take different stands on how greatly language can influence emotional experience. William James’s peripheral feedback theory, Paul Ekman’s basic emotions theory, Magda Arnold’s appraisal theory, and Lisa Feldman Barrett’s conceptual act theory offer distinct frameworks for understanding how physiology and culture interact in human emotions. The research of Max Black, George Lakoff, and Zoltán Kövecses indicates that emotion metaphors have bodily and cultural roots. Dante Alighieri’s Inferno and John Bunyan’s The Pilgrim’s Progress illustrate the religious origin of metaphors for culturally “banned” emotions. Traces of these religious origins can be seen in the metaphors of self-help books such as Daniel Goleman’s Emotional Intelligence , Travis Bradberry’s and Jean Greaves’s Emotional Intelligence 2.0 , and Spencer Johnson’s Who Moved My Cheese? A long-standing cultural tradition presumes there is a self separate from the emotions that is responsible for controlling them, but scientific studies point toward emotional regulation within a self.Chapter The Bodily and Cultural Roots of Emotion Metaphors

Social interaction between players is an important feature in online games, where text- and voice chat is a standard way to communicate. To express emotions players can type emotes which are text-based commands to play animations from the player avatar. This paper presents a perceptual evaluation which investigates if instead expressing emotions with the face, in real-time with a web camera, is perceived more realistic and preferred in comparison to typing emote-based text commands. A user study with 24 participants was conducted where the two methods to express emotions described above were evaluated. For both methods the participants ranked the realism of facial expressions, which were based on the theory of seven universal emotions stated by American psychologist Paul Ekman: happiness, anger, fear, sadness, disgust, surprise and contempt. The participants also ranked their perceived efficiency of performing the two methods and selected the method they preferred. A significant difference was shown when analyzing the results of ranked facial expression realism. Happiness was perceived as the most realistic in both methods, while disgust and sadness were poorly rated when performed with the face. One conclusion of the perceptual evaluation was that the realism and preference between the methods showed no significant differences. However, participants had higher performance in typing with emotes. Real-time facial capture technology also needs improvements to obtain better recognition and tracking of facial features in the human face.

Conference Paper A perceptual evaluation of social interaction with emotes an...

Detecting emotions in tweets is a huge challenge due to its limited 140 characters and extensive use of twitter language with evolving terms and slangs. This paper uses various preprocessing techniques, forms a feature vector using lexicons and classifies tweets into Paul Ekman's basic emotions namely, happy, sad, anger, fear, disgust and surprise using machine learning. Preprocessing is done using the dictionaries available for emoticons, interjections and slangs and by handling punctuation marks and hashtags. The feature vector is created by combining words from the NRC Emotion lexicon, WordNet-Affect and online thesaurus. Feature vectors are assigned weight based on the presence of punctuations and negations in the feature and the tweets are classified using naive Bayes, SVM and random forests. The use of lexicon features and a novel weighting scheme has produced a considerable gain in terms of accuracy with random forest achieving maximum accuracy of almost 73%.Article A lexicon-based term weighting scheme for emotion identifica...

Researchers from different areas has an interest in emotions, even if their focus are distinct this subject has a direct impact in the behavior and coexistence of human beings. Paul Ekman discovered the existence of six universal emotions: happiness; sadness; fear; disgust; anger; and surprise. Based on this study, Paul Ekman developed a Facial Action Coding System (FACS). There are a lot of works that utilize the FACS to represent computationally facial expressions and consequently the emotions. Because of the limitations found in the process of facial expressions recognition, more specifically the dependence of the luminosity’s ideal conditions, this work presents a solution using Kinect-V2 as a way of infrared image extraction, the Viola-Jones algorithm to face detection and posterior image crop and data classification using Support Vector Machine (SVM) based on FACS. At the end of the process the success rate were about 69.96% using the data base generated in this work. The biggest rate were 95.21% obtained with happiness expression.

Thesis RECONHECENDO EXPRESSÕES FACIAIS HUMANAS EM AMBIENTES DE BAIX...

2021

As massive changes in our lifestyle and society structure are coming, due to the contemporary pandemic situation, we face more than ever the challenge of employing non-human and teleassistance devices in many areas, especially those that require assisting and caring for weak users. Humanoid robots are proven to be a valuable asset in these situations but require to be carefully designed in their interaction features, in order to be accepted and valuably used by their users. In particular, this study is focused on robots that have a certain degree of human-likeness that allows to define them “humanoids”; for this kind of robots we can say that the area devoted to replicate human facial features is the most important interface for human–robot interaction. Actually, more than 60% of human–human interaction is conducted non-verbally, by using facial expressions and gestures. For a robot to be able to engage in this kind of interaction with a human and provide understandable feedbacks is a massive step towards acceptance and development of an affectional relationship by users. Being meant to reproduce human emotions, visual feedbacks are mostly developed referring to eminent researches in the psychological field: as Paul Ekman already observed in 1998, human faces have a universal coding for six basic expressions that represent as many basic emotions: fear, anger, disgust, happiness, sadness and surprise. Our research focuses on the design of an expression system to be implemented in a European-funded project for an assistive robot that will support weak users at home or in assistance facilities and their caregivers. Our main concern regarding this project are to design a dynamic, human-friendly system that is scalable and visually recalls real facial expressions without being too much human-like, in order to avoid the well-known uncanny effect described by Masairo Mori.

Chapter Designing Synthetic Emotions of a Robotic System

Awareness of the emotion in human–computer interaction is a challenging task when building human-centered computing systems. Emotion is a complex state of mind that is affected by external events, physiological changes and, generally, human relationships. Researchers suggest various methods of measuring human emotions through the analysis of physiological signals, facial expressions, voice, etc. This chapter presents a system for recognizing the emotions of a smartphone user through the collection and analysis of data generated from different types of sensors on the device. Data collection is carried out by an application installed on the participants’ smartphone provided that the smartphone remains in their pocket throughout the experiments. The collected data are processed and utilized to train different classifiers (decision trees, naïve Bayes and k-nearest neighbors). Emotions are classified in the following six categories: happiness, neutral, sadness, disgust, fear, surprise. Initial results show that the system classifies user’s emotions with 82.83% accuracy. The proposed system applied to a smartphone demonstrates the feasibility of an emotion recognition approach through a user-friendly scenario for users’ activity recognition. Chapter Passive Emotion Recognition Using Smartphone Sensing Data

Facial mimicry is described by embodied cognition theories as a human mirror system-based neural mechanism underpinning emotion recognition. This could play a critical role in the Self-Mirroring Technique (SMT), a method used in psychotherapy to foster patients’ emotion recognition by showing them a video of their own face recorded during an emotionally salient moment. However, dissociation in facial mimicry during the perception of own and others’ emotions has not been investigated so far. In the present study, we measured electromyographic (EMG) activity from three facial muscles, namely, the zygomaticus major (ZM), the corrugator supercilii (CS), and the levator labii superioris (LLS) while participants were presented with video clips depicting their own face or other unknown faces expressing anger, happiness, sadness, disgust, fear, or a neutral emotion. The results showed that processing self vs. other expressions differently modulated emotion perception at the explicit and implicit muscular levels. Participants were significantly less accurate in recognizing their own vs. others’ neutral expressions and rated fearful, disgusted, and neutral expressions as more arousing in the self condition than in the other condition. Even facial EMG evidenced different activations for self vs. other facial expressions. Increased activation of the ZM muscle was found in the self condition compared to the other condition for anger and disgust. Activation of the CS muscle was lower for self than for others’ expressions during processing a happy, sad, fearful, or neutral emotion. Finally, the LLS muscle showed increased activation in the self condition compared to the other condition for sad and fearful expressions but increased activation in the other condition compared to the self condition for happy and neutral expressions. Taken together, our complex pattern of results suggests a dissociation at both the explicit and implicit levels in emotional processing of self vs. other emotions that, in the light of the Emotion in Context view, suggests that STM effectiveness is primarily due to a contextual–interpretative process that occurs before that facial mimicry takes place. Article Explicit and Implicit Responses of Seeing Own vs. Others’ Em...

Article Changes in vocal emotion recognition across the life span

Article The visual perception of emotion from masks

Article Facial expressions and the ability to recognize emotions fro...

Article Facial expressions and the ability to recognize emotions fro...

Article Facial Sadness Recognition is Modulated by Estrogen Receptor...

Solaiman Elsheikh Thank you so much for all this great information! God bless :)