Facial Expression and Emotion Paul Ekman
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Neural Networks for Emotion Classification
Neural Networks for Emotion Classification For obtaining master degree In computer science At Leiden Institute of Advanced Computer Science Under supervision of Dr. Michael S. Lew and Dr. Nicu Sebe by Yafei Sun August 2003 1 ACKNOWLEDGMENTS I would like to express my heartfelt gratitude to Dr. Michael S. Lew for his support, invaluable guidance, time, and encouragement. I’d like to express my sincere appreciation to Dr. Nicu Sebe for his countless ideas and advice, his encouragement and sharing his valuable knowledge with me. Thanks to Ernst Lindoorn and Jelle Westbroek for their help on the experiments setup. Thanks to all the friends who participated in the construction of the authentic emotion database. I would also like to Dr. Andre Deutz, Miss Riet Derogee and Miss Rachel Van Der Waal for helping me out with a problem on the scheduling of my oral presentation of master thesis. Finally a special word of thanks to Dr. Erwin Bakker for attending my graduation exam ceremony. 2 Neural Networks for Emotion Classification Table of Contents ABSTRACT ............................................................................................................................................5 1 INTRODUCTION ..........................................................................................................................5 2 BACKGROUND RESEARCH IN EMOTION RECOGNITION..............................................8 2.1 AN IDEAL SYSTEM FOR FACIAL EXPRESSION RECOGNITION: CURRENT PROBLEMS.....11 2.2 FACE DETECTION AND FEATURE EXTRACTION................................................................11 -
Truth Be Told
Truth Be Told . - washingtonpost.com Page 1 of 4 Hello vidal Change Preferences | Sign Out Print Edition | Subscribe NEWS POLITICS OPINIONS LOCAL SPORTS ARTS & LIVING CITY GUIDE JOBS SHO SEARCH: washingtonpost.com Web | Search Ar washingtonpost.com > Print Edition > Sunday Source » THIS STORY: READ + | Comments Truth Be Told . Sunday, November 25, 2007; Page N06 In the arena of lie detecting, it's important to remember that no emotion tells you its source. Think of Othello suffocating Desdemona because he interpreted her tears (over Cassio's death) as the reaction of an adulterer, not a friend. Othello made an assumption and killed his wife. Oops. The moral of the story: Just because someone exhibits the behavior of a liar does not make them one. "There isn't a silver bullet," psychologist Paul Ekman says. "That's because we don't have Pinocchio's nose. There is nothing in demeanor that is specific to a lie." Nevertheless, there are indicators that should prompt you to make further inquiries that may lead to discovering a lie. Below is a template for taking the first steps, gleaned from interviews with Ekman and several other experts, as well as their works. They are psychology professors Maureen O'Sullivan (University of San Francisco), (ISTOCKPHOTO) Robert Feldman (University of Massachusetts) and Bella DePaulo (University of California at Santa Barbara); TOOLBOX communication professor Mark Frank (University at Resize Text Save/Share + Buffalo); and body language trainer Janine Driver (a.k.a. Print This the Lyin' Tamer). E-mail This COMMENT How to Detect a Lie No comments have been posted about this http://www.washingtonpost.com/wp-dyn/content/article/2007/11/21/AR2007112102027... -
The Development of Emotion Recognition from Facial Expressions and Non‐Linguistic Vocalizations During Childhood
1 British Journal of Developmental Psychology (2014) © 2014 The British Psychological Society www.wileyonlinelibrary.com The development of emotion recognition from facial expressions and non-linguistic vocalizations during childhood Georgia Chronaki1,2*, Julie A. Hadwin2, Matthew Garner3, Pierre Maurage4 and Edmund J. S. Sonuga-Barke2,5 1Section of Clinical and Cognitive Neuroscience, School of Psychological Sciences, University of Manchester, UK 2Developmental Brain-Behaviour Laboratory, Psychology, University of Southampton, UK 3Psychology, Faculty of Social & Human Sciences & Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, UK 4Laboratory for Experimental Psychopathology, Psychological Sciences Research Institute, Catholic University of Louvain, Louvain-la-Neuve, Belgium 5Department of Experimental Clinical and Health Psychology, Ghent University, Belgium Sensitivity to facial and vocal emotion is fundamental to children’s social competence. Previous research has focused on children’s facial emotion recognition, and few studies have investigated non-linguistic vocal emotion processing in childhood. We compared facial and vocal emotion recognition and processing biases in 4- to 11-year-olds and adults. Eighty-eight 4- to 11-year-olds and 21 adults participated. Participants viewed/listened to faces and voices (angry, happy, and sad) at three intensity levels (50%, 75%, and 100%). Non-linguistic tones were used. For each modality, participants completed an emotion identification task. Accuracy and bias for each emotion and modality were compared across 4- to 5-, 6- to 9- and 10- to 11-year-olds and adults. The results showed that children’s emotion recognition improved with age; preschoolers were less accurate than other groups. Facial emotion recognition reached adult levels by 11 years, whereas vocal emotion recognition continued to develop in late childhood. -
Recognising Spontaneous Facial Micro-Expressions
Recognising Spontaneous Facial Micro-expressions Tomas Pfister, Xiaobai Li, Guoying Zhao and Matti Pietik¨ainen Machine Vision Group, Department of Computer Science and Engineering, University of Oulu PO Box 4500, 90014 Oulu, Finland {tpfister,lxiaobai,gyzhao,mkp}@ee.oulu.fi Abstract Facial micro-expressions are rapid involuntary facial ex- pressions which reveal suppressed affect. To the best knowl- edge of the authors, there is no previous work that success- fully recognises spontaneous facial micro-expressions. In this paper we show how a temporal interpolation model together with the first comprehensive spontaneous micro- expression corpus enable us to accurately recognise these very short expressions. We designed an induced emotion Figure 1. An example of a facial micro-expression (top-left) be- suppression experiment to collect the new corpus using a ing interpolated through graph embedding (top-right); the result high-speed camera. The system is the first to recognise from which spatiotemporal local texture descriptors are extracted spontaneous facial micro-expressions and achieves very (bottom-right), enabling recognition with multiple kernel learning. promising results that compare favourably with the human micro-expression detection accuracy. proposed a suitable price. Since the human recognition accuracy is so low, an alternative method for recognising 1. Introduction micro-expressions would be very valuable. Humans are good at recognising full facial expressions The major challenges in recognising micro-expressions which present a rich source of affective information [6]. involve their very short duration and involuntariness. The However, psychological studies [4, 8] have shown that af- short duration means only a very limited number of frames fect also manifests itself as micro-expressions. -
Visual Perception of Facial Emotional Expressions During Saccades
behavioral sciences Article Visual Perception of Facial Emotional Expressions during Saccades Vladimir A. Barabanschikov and Ivan Y. Zherdev * Institute of Experimental Psychology, Moscow State University of Psychology and Education, 29 Sretenka street, Moscow 127051, Russia; [email protected] * Correspondence: [email protected]; Tel.: +7-999-829-52-79 Received: 28 October 2019; Accepted: 23 November 2019; Published: 27 November 2019 Abstract: The regularities of visual perception of both complex and ecologically valid objects during extremely short photo expositions are studied. Images of a person experiencing basic emotions were displayed for as low as 14 ms amidst a saccade spanning 10 degrees of visual angle. The observers had a main task to recognize the emotion depicted, and a secondary task to point at the perceived location of the photo on the screen. It is shown that probability of correct recognition of emotion is above chance (0.62), and that it depends on its type. False localizations of stimuli and their compression in the direction of the saccade were also observed. According to the acquired data, complex environmentally valid objects are perceived differently during saccades in comparison to isolated dots, lines or gratings. The rhythmic structure of oculomotor activity (fixation–saccade–fixation) does not violate the continuity of the visual processing. The perceptual genesis of facial expressions does not take place only during gaze fixation, but also during peak speed of rapid eye movements both at the center and in closest proximity of the visual acuity area. Keywords: transsaccadic processing; facial expression; emotional perception; saccadic suppression; gaze contingency; visual recognition 1. -
Ekman, Emotional Expression, and the Art of Empirical Epiphany
JOURNAL OF RESEARCH IN PERSONALITY Journal of Research in Personality 38 (2004) 37–44 www.elsevier.com/locate/jrp Ekman, emotional expression, and the art of empirical epiphany Dacher Keltner* Department of Psychology, University of California, Berkeley, 3319 Tolman, 94720 Berkeley, CA, USA Introduction In the mid and late 1960s, Paul Ekman offered a variety of bold assertions, some seemingly more radical today than others (Ekman, 1984, 1992, 1993). Emotions are expressed in a limited number of particular facial expressions. These expressions are universal and evolved. Facial expressions of emotion are remarkably brief, typically lasting 1 to 5 s. And germane to the interests of the present article, these brief facial expressions of emotion reveal a great deal about peopleÕs lives. In the present article I will present evidence that supports this last notion ad- vanced by Ekman, that brief expressions of emotion reveal important things about the individualÕs life course. To do so I first theorize about how individual differences in emotion shape the life context. With this reasoning as backdrop, I then review four kinds of evidence that indicate that facial expression is revealing of the life that the individual has led and is likely to continue leading. Individual differences in emotion and the shaping of the life context People, as a function of their personality or psychological disorder, create the sit- uations in which they act (e.g., Buss, 1987). Individuals selectively attend to certain features of complex situations, thus endowing contexts with idiosyncratic meaning. Individuals evoke responses in others, thus shaping the shared, social meaning of the situation. -
E Motions in Process Barbara Rauch
e_motions in process Barbara Rauch Abstract This research project maps virtual emotions. Rauch uses 3D-surface capturing devices to scan facial expressions in (stuffed) animals and humans, which she then sculpts with the Phantom Arm/ SensAble FreeForm device in 3D virtual space. The results are rapidform printed objects and 3D animations of morphing faces and gestures. Building on her research into consciousness studies and emotions, she has developed a new artwork to reveal characteristic aspects of human emotions (i.e. laughing, crying, frowning, sneering, etc.), which utilises new technology, in particular digital scanning devices and special effects animation software. The proposal is to use a 3D high-resolution laser scanner to capture animal faces and, using the data of these faces, animate and then combine them with human emotional facial expressions. The morphing of the human and animal facial data are not merely layers of the different scans but by applying an algorithmic programme to the data, crucial landmarks in the animal face are merged in order to match with those of the human. The results are morphings of the physical characteristics of animals with the emotional characteristics of the human face in 3D. The focus of this interdisciplinary research project is a collaborative practice that brings together researchers from UCL in London and researchers at OCAD University’s data and information visualization lab. Rauch uses Darwin’s metatheory of the continuity of species and other theories on evolution and internal physiology (Ekman et al) in order to re-examine previous and new theories with the use of new technologies, including the SensAble FreeForm Device, which, as an interface, allows for haptic feedback from digital data.Keywords: interdisciplinary research, 3D-surface capturing, animated facial expressions, evolution of emotions and feelings, technologically transformed realities. -
Emotion Classification Based on Biophysical Signals and Machine Learning Techniques
S S symmetry Article Emotion Classification Based on Biophysical Signals and Machine Learning Techniques Oana Bălan 1,* , Gabriela Moise 2 , Livia Petrescu 3 , Alin Moldoveanu 1 , Marius Leordeanu 1 and Florica Moldoveanu 1 1 Faculty of Automatic Control and Computers, University POLITEHNICA of Bucharest, Bucharest 060042, Romania; [email protected] (A.M.); [email protected] (M.L.); fl[email protected] (F.M.) 2 Department of Computer Science, Information Technology, Mathematics and Physics (ITIMF), Petroleum-Gas University of Ploiesti, Ploiesti 100680, Romania; [email protected] 3 Faculty of Biology, University of Bucharest, Bucharest 030014, Romania; [email protected] * Correspondence: [email protected]; Tel.: +40722276571 Received: 12 November 2019; Accepted: 18 December 2019; Published: 20 December 2019 Abstract: Emotions constitute an indispensable component of our everyday life. They consist of conscious mental reactions towards objects or situations and are associated with various physiological, behavioral, and cognitive changes. In this paper, we propose a comparative analysis between different machine learning and deep learning techniques, with and without feature selection, for binarily classifying the six basic emotions, namely anger, disgust, fear, joy, sadness, and surprise, into two symmetrical categorical classes (emotion and no emotion), using the physiological recordings and subjective ratings of valence, arousal, and dominance from the DEAP (Dataset for Emotion Analysis using EEG, Physiological and Video Signals) database. The results showed that the maximum classification accuracies for each emotion were: anger: 98.02%, joy:100%, surprise: 96%, disgust: 95%, fear: 90.75%, and sadness: 90.08%. In the case of four emotions (anger, disgust, fear, and sadness), the classification accuracies were higher without feature selection. -
Lecture 2: Theories of Emotion While We Wait Outline
Lecture 2: Theories of Emotion While we wait Outline . Review why emotion theory useful – Give some positive and negative examples . Introduce some features that distinguish different theories – Emotions as discrete or continuious – Emotions as “atoms” or “molecules” – Emotions as a consequence or antecedent of emotin . Review some specific influential theories . Break . In-class “experiment” . Evidence for and against dual-process models of emotion . Appraisal Theory . Review a mental health application of affective computing Why should we care about emotion theory? What is a Theory . Theory explains how some aspect of human behavior or performance is organized. It thus enables us to make predictions about that behavior. – Provides a set of interrelated concepts, definitions, and propositions that explains or predicts aspects of human behavior by specifying relations among variables. – Allows us to explain what we see and to figure out how to bring about change. – Is a tool that enables us to identify a problem and to plan a means for altering the situation. – Create a basis for future research. Researchers use theories to form hypotheses that can then be tested. – Creates a basis for building software: suggests what variables are important to measure and how they relate to each other Example of dangers of atheoretical approaches (e.g., Data Mining) . We’ll learn about machine learning approaches – Collect bunch of data – Look at lots of features and try to predict some outcome Input Predicted features output . Enables us to make predictions about that behavior . Does not typically allows us to explain what we see and to figure out how to bring about change . -
Facial Micro-Expression Analysis Jingting Li
Facial Micro-Expression Analysis Jingting Li To cite this version: Jingting Li. Facial Micro-Expression Analysis. Computer Vision and Pattern Recognition [cs.CV]. CentraleSupélec, 2019. English. NNT : 2019CSUP0007. tel-02877766 HAL Id: tel-02877766 https://tel.archives-ouvertes.fr/tel-02877766 Submitted on 22 Jun 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. THESE DE DOCTORAT DE CENTRALESUPELEC COMUE UNIVERSITE BRETAGNE LOIRE ECOLE DOCTORALE N° 601 Mathématiques et Sciences et Technologies de l'Information et de la Communication Spécialité : Signal, Image, Vision Par « Jingting LI » « Facial Micro-Expression Analysis » Thèse présentée et soutenue à « Rennes », le « 02/12/2019 » Unité de recherche : IETR Thèse N° : 2019CSUP0007 Rapporteurs avant soutenance : Composition du Jury : Olivier ALATA Professeur, Université Jean Monnet, Président / Rapporteur Saint-Etienne Olivier ALATA Professeur, Fan YANG-SONG Professeur, Université de Bourgogne, Université Jean Monnet, Dijon Saint-Etienne Rapporteuse Fan YANG-SONG Professeur, Université de Bourgogne, -
Desiring, Departing and Dying
THE BIBLE & CRITICAL THEORY Desiring, Departing and Dying Affectively Speaking: Epithymia in Philippians 1:23 Sharday C. Mosurinjohn and Richard S. Ascough, Queen’s University Abstract In a text that has caused no shortage of speculation and consternation, Paul links “desire” (epithymia) with “death” in writing of his “desire to depart and be with Christ” (Phil. 1:23). While “desire” has mostly negative valences in Paul’s letters, often linked to sexual craving, here it is linked to his broad fascination with death that is apparent in his frequent references not only to Christ’s crucifixion but also more generally both to physical death and metaphorical death. Paul’s contemplation of which is preferable, life or death, raises questions about how issues of social and existential meaning are affectively negotiated under the aspect of death, under what conditions one desires death, and what is the nature of desire itself. Thinking in this vein suggests an ecology of instincts vital to the affective valences of desiring death. This paper will explore these issues to show how Paul’s own desire may be expressing both a passionate pull to his idea of Christ and a longing to escape that very passion. Keywords Paul; Desire; Death; Affect Theory; Philippians “The anticipation of our own death tells us more about anticipation than it does about death.” (Phillips 2000, 110) Introduction I, (Richard), wrote a disaster of a paper a few years ago, which I presented at a conference and opened with the words, “Well, this was a failed experiment …” It was an attempt to understand an enigmatic, autobiographical clip in one of the letters written by Paul. -
A Silvan Tomkins Handbook Foundations for Affect Theory Adam J
A Silvan Tomkins Handbook Foundations for Affect Theory Adam J. Frank, University of British Columbia Elizabeth Wilson, Emory University Publisher: University of Minnesota Press Publication Place: Minneapolis, MN Publication Date: 2020 Edition: 1 Type of Work: Book | Final Publisher PDF Permanent URL: https://pid.emory.edu/ark:/25593/vgvtq Final published version: https://lccn.loc.gov/2020020378 Copyright information: 2020 by Adam J. Frank and Elizabeth A. Wilson This is an Open Access work distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/). Accessed October 4, 2021 2:59 AM EDT SURPRISE — STARTLE INTEREST — EXCITEMENT ENJOYMENT — JOY A SHAME — HUMILIATION SILVAN TOMKINS Handbook DISTRESS — ANGUISH FOUNDATIONS FOR AFFECT THEORY ADAM J. FRANK ELIZABETH A. WILSON DISGUST and FEAR — TERROR ANGER — RAGE DISSMELL A Silvan Tomkins Handbook A SILVAN TOMKINS HANDBOOK Foundations for Affect Theory Adam J. Frank and Elizabeth A. Wilson University of Minnesota Press Minneapolis | London This book is freely available in an open access edition thanks to TOME (Toward an Open Monograph Ecosystem)—a collaboration of the Association of American Universities, the Association of University Presses, and the Association of Research Libraries—and the generous support of Emory University and the Andrew W. Mellon Foundation. Learn more at the TOME website, available at: openmonographs.org. Copyright 2020 by Adam J. Frank and Elizabeth A. Wilson A Silvan Tomkins Handbook: Foundations for Affect Theory is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0): https://creativecommons.org/licenses/by-nc-nd/4.0/.